AI is rapidly evolving, and in order to stay relevant it’s important that you match the pace and keep yourself updated with the latest AI advancements. 

To facilitate this, The Association of Data Scientists (ADaSci) offers a variety of AI courses designed to cater to different expertise levels, from mastering LangChain and building AI agents to understanding RAG and parameter-efficient fine-tuning. 

Whether you’re a beginner in the GenAI field or a seasoned AI professional, these courses provide hands-on experience and detailed knowledge to keep you ahead in the game. ADaSci’s unique courses are not available anywhere else. 

Discover the top 12 AI courses available on ADaSci and unlock new opportunities. 

Generative AI Crash Course with Hands-on Implementations

This course will help you get an in-depth understanding of GenAI and its popular models. Participants will receive a detailed knowledge of GPT models, diffusion models, different NLP transformers and ChatGPT. The course will further provide you with a hands-on knowledge of implementing GenAI models in real-world applications.

This course caters to everyone, from beginners in GenAI looking to deepen their understanding and practical skills to professionals in AI and related fields seeking to update their knowledge with the latest advancements in GenAI. 

Mastering LangChain: A Hands-on Workshop for Building Generative AI Applications

This LangChain workshop will help participants master GenAI for innovative applications across industries. You will learn to build and deploy custom AI agents, leveraging LangChain for transformative personalised solutions. 

Participants should have a foundational understanding of AI and basic programming skills, preferably in Python. 

Diving Deeper into Retrieval-Augmented Generation (RAG) with Vector Databases

This course will help you master the core principles of RAG and its advantages over pure generative models. Participants will delve into advanced AI techniques, unlocking the synergy between RAG and vector databases. You will also understand the tools and strategies for building, deploying, and optimising RAG systems. 

Parameter-efficient Fine-tuning of Large Language Models

This workshop will help you understand Parameter-efficient fine-tuning (PEFT) techniques and their benefits for LLM adaptation. Participants will learn methods like LoRA, adapters, and prompt tuning to achieve remarkable results using less parameters. 

You will also get hands-on experience building and evaluating your own PEFT model on provided datasets. With this course, you can master resource-efficient training strategies and deployment options for PEFT models. 

Building Generative AI Applications with Amazon Bedrock

This hands-on course will provide you with a solid understanding of the Amazon Bedrock architecture, capabilities, and applications. It will help participants develop skills in building and deploying GenAI applications on Bedrock, allowing them to gain insights into real-world use cases, best practices, and the future potential of Bedrock. 

Mastering Prompt Engineering for LLMs

With this course, participants will understand the fundamentals of prompt engineering and master the art of crafting, optimising, and customising prompts for various AI models. 

It will help you explore various prompting concepts and techniques such as Zero-shot and Few-shot Prompting, Chain of Thought Prompting, Knowledge Generation Prompting, and more. 

The LLMOps : Streamlining the GenAI & LLM Operations

This course can be beneficial in understanding the fundamentals of LLMOps and its role in GenAI-powered systems and NLP. 

Participants will develop knowledge about the workings of LLMOps and explore its challenges such as model training, deployment, monitoring, and maintenance. They will also learn the design process of LLMOps and acquire practical skills in innovating within the LLMOps operations. 

Autonomous AI Agents and AI Copilots

This course will teach you the foundational concepts behind building AI agents and delve into different ML techniques that make them smarter. It also examines the challenges of creating dependable AI agents and the ethical considerations that come with them. 

Through this course, you’ll be able to analyse the potential benefits and limitations of autonomous AI agents and AI copilots in different application domains such as healthcare, finance, creative work etc. 

You will also understand various techniques in autonomous AI agents and copilots such as BabyAGI, MetaGPT, and Semantic Kernels. 

Advanced RAG with Pinecone 

This course will take your text generation skills to the next level. It will help you master the utilisation of Pinecone for information retrieval in RAG. 

You’ll learn about integrating knowledge bases and crafting powerful prompts, creating informative and creative text outputs. 

Building Multi-Agent LLMs with AutoGen

With this course, you’ll learn how to build multi-agent LLMs and create collaborative AI systems using the AutoGen framework. 

It will also help you unlock real-world applications, exploring how multi-agent LLMs can be applied in various domains for problem-solving. 

Vector Search Techniques with Weaviate

This course will help you explore advanced vector search techniques using Weaviate, a vector search engine. You will learn about Weaviate’s architecture, features, and capabilities for vector-based search and semantic querying.

Participants will dive into hands-on exercises to master indexing, querying, and optimising vector search performance. 

Generative AI Application Development with Azure

This course equips you with essential skills to develop, deploy, and monitor GenAI applications using Microsoft Azure. You will gain hands-on experience with Azure’s powerful AI services, enhance your technical expertise, and learn to develop scalable AI solutions. 

The post Top 12 Generative AI Courses Available on ADaSci appeared first on AIM.

A new era of autonomous AI agents has begun. The latest developments from Google I/O 2024 and OpenAI’s Spring Update confirm that all major companies are bullish on agents and that the future of AI will be ‘agentic’.

“Honestly, the path to AGI feels like a journey rather than a destination, but I think agent workflows could help us take a small step forward on this very long journey,” said Andrew Ng. 

AI Agents will be ubiquitous, and the best part is that you can build them too!

Here are the top 10 free courses to help you build next-gen consumer and enterprise agent workflows that can enhance your productivity and help you solve complex problems.

Multi AI Agent Systems with crewAI

Recently, Andrew Ng unveiled this agentic short course, ‘Multi AI Agent Systems with crewAI’. 

Available on DeepLearning.AI and built in collaboration with crewAI’s founder and CEO João Moura, it will teach you how to break down complex tasks into subtasks for specialised AI agents. Alongside this, it helps in discovering how to define roles, set expectations, and manage interactions among multiple AI agents.

The course also touches upon key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. It also helps you build and manage your own multi-agent systems to tackle complex tasks effectively.

By the end of the course, you will also be equipped to design and deploy multi-agent architectures, driving significant progress in AI systems. 

Building RAG Agents with LLMs

This course by NVIDIA is perfect for developers looking to create advanced AI agents. 

In this free course, you’ll learn to deploy scalable agent systems powered by LLMs and discover how LLMs excel in tool use, document interaction, and strategic planning.

You’ll also implement and evaluate RAG agents for answering research paper queries. Key topics include LLM inference interfaces, pipeline design with LangChain, Gradio, LangServe, dialogue management, and vector stores for RAG agents. By the end, you’ll have the tools to develop advanced LLM applications.

Build Agents with GPT-4o From Scratch

Learn how to build agents for multiple uses, including web search, finance, Hacker News, data analysis, and research with GPT-4o from scratch in just 11 minutes.  

Build AI Agents Smarter Than ChatGPT

The video delves into the concept of building AI agents that surpass current models like ChatGPT in functionality, focusing on their potential to complete extensive tasks quickly and autonomously. 

It introduces Agency Swarm, a new framework designed for real-world business applications. It’s unique for its customisation capabilities and ease of use when building smarter AI agents.

It includes step-by-step instructions, and even those without programming skills can watch the course to learn how to adopt AI technology to stay ahead in a rapidly evolving field.

The Right Way to Build AI Agents With crewAI

This video delves into the best practices for building AI agents with crewAI, focusing on a fully local setup. It starts with an overview of crewAI’s capabilities and its approach to creating intelligent agents.

You’ll learn how to set up a local environment, define agent roles, and manage interactions between agents for various tasks. The tutorial includes practical coding demonstrations, showcasing how to implement these agents for diverse tasks. By the end, you’ll be equipped to develop efficient, scalable AI agents using crewAI’s platform, all while keeping your setup entirely local.

Create AI Agents From Scratch With Python 

This video will teach you how to create AI agents from scratch using Python. It covers the fundamentals of building intelligent agents capable of decision-making and learning. You’ll learn about the key components of AI agents, including perception, action, and decision-making processes. 

The tutorial then dives into coding examples, demonstrating how to implement various AI algorithms for searching, reinforcement learning, and decision trees. By the end, you’ll be equipped with the knowledge to build and deploy your own intelligent agents for a variety of applications.

Building Agentic RAG with LlamaIndex

Taught by Jerry Liu, CEO of LlamaIndex, this course ‘Building Agentic RAG with LlamaIndex’, is available on DeepLearning.AI. It will teach you to build a RAG agent with tool access for autonomous information retrieval, enabling it to answer complex questions using multi-step reasoning.

It will cover key aspects such as tool use, multi-step reasoning with tool use, and routing, where your agent will use decision-making to route requests to multiple tools. Additionally, you’ll also learn to debug and iteratively improve your agent.

CampusX: Building AI Agents

In this course, you’ll learn how to create advanced AI agents using tools like crewAI, AutoGen, LangGraph, and AutoGPT. This course dives into AI development, teaching you to build intelligent agents capable of performing complex tasks and enhancing automation processes. 

Key highlights include utilising these technologies for AI agent development, creating agents with advanced capabilities, and optimising their performance. You’ll explore how to implement AI agents to improve automation across various industries and discover techniques to optimise their performance, ensuring they operate efficiently and effectively.

https://learnwith.campusx.in/courses/Building-AI-Agents-663d25be012c994c18513e70

Functions, Tools, and Agents with LangChain

This short course is presented in collaboration with LangChain and is available on DeepLearning.AI.  It is taught by LangChain CEO Harrison Chase. 

This course explores the capabilities of LLMs to call functions essential for managing structured data and form a foundation for LLM-based agents.

You’ll utilise LangChain’s expression language to develop applications that handle tagging, expression extraction, tool selection, and routing. Additionally, you’ll create a conversational agent showcasing all these features.

The Complete Guide to Building AI Agents for Beginners

In this video, you’ll learn how to develop custom AI agent systems for companies of all sizes, from small firms to large corporations. 

By the end of this video, users will be able to build their own fully functional social media marketing agency that will generate ad copy, create ad images with DALL-E 3, and reliably post them on Facebook.

The post 10 Free Courses to Build AI Agents in 2024 appeared first on AIM.

Digitisation has paved the way for data science to swiftly become a highly sought-after career path. Businesses worldwide are eager to harness the power of data professionals to maximise their data’s potential and improve business performance.

Register for the Free Workshop: Advanced Data Engineering with Snowflake >

As AI and data science become crucial across all types of organisations, pursuing a career in this field has become easier. To meet this rising demand, esteemed institutions such as the IITs and IIMs have begun integrating departments focused on data science and AI, offering courses at different proficiency levels.

We have carefully curated a list of institutions in India that teach data science and artificial intelligence.

1. Wadhwani School of Data Science and Artificial Intelligence, IIT Madras

Just a few months ago, Sunil Wadhwani, a distinguished alumnus of IIT Madras and co-founder of iGATE and Mastech Digital, donated ₹110 crore to establish the Wadhwani School of Data Science. It endeavours to impart knowledge not only on the intricacies of AI algorithms, but also the underlying systems driving AI, along with proficiency in data gathering and management. 

The curriculum is structured into four main components: Foundational sciences, modelling techniques, training and deploying models, and ultimately, integrating these insights into real-world applications. 

The school offers a diverse range of courses, including BTech and MTech programs in data science and AI, an interdisciplinary dual degree program in data science, MS and PhD programs, a joint MSc in data science and AI with the University of Birmingham, and a web-enabled MTech program in industrial AI.

Recently, Balraman Ravindran shared a post unveiling his journey at Wadhwani School.

2. Centre for Machine Intelligence and Data Science, IIT Mumbai

The Centre for Machine Intelligence and Data Science (C-MInDS) is at the forefront of AI and ML exploration. Through groundbreaking research, academic programs, and strong industry collaborations, C-MInDS is expanding the horizons of these technologies and exploring their real-world uses.

Their academic offerings span a wide spectrum, catering to students at various stages of their educational journey. From foundational courses in AI for undergraduates to advanced postgraduate and doctoral studies, they provide a comprehensive range of programs. 

3. Yardi School of Artificial Intelligence, IIT Delhi

The Yardi School of Artificial Intelligence (Yardi ScAI) was established in September 2020 at IIT Delhi with a mission to advance education and research in artificial intelligence, machine learning, and data science, along with their diverse applications in fields such as healthcare, materials science, robotics, industry 4.0, weather prediction, and transportation.

This interdisciplinary school boasts support from over 40 faculty members across various departments, reflecting its broad focus on applications. Yardi ScAI offers a range of programs, their MINDS program caters to industry needs, while the MS(R) program is tailored for those inclined towards research.

4. Machine Intelligence and Robotics COE (MINRO), IIIT Bangalore

The MINRO Center has a broad mandate: to conduct top-tier research in machine intelligence and robotics, with the aim of producing groundbreaking innovations that benefit both Karnataka and the nation.

The centre is dedicated to multidisciplinary research and development across key areas such as machine intelligence, artificial intelligence systems, data analysis, data science, pattern recognition, human-machine interface, and industrial products related to robotics and automation.

5. Mehta Family School of Data Science and Artificial Intelligence, IIT Guwahati

Established in 2021 at IIT Guwahati and supported by the Mehta Family Foundation, the school aims to become a leading institution dedicated to generating knowledge through the analysis of diverse datasets and the development of intelligence engineering methodologies. 

Since its inception, the school has introduced BTech and PhD programs in Data Science & Artificial Intelligence. Building on this foundation, in 2023, Mehta Family expanded their offerings to include an MTech program in Data Science, offered jointly with the departments of mathematics and electrical engineering. 

6. The Centre of Excellence in Artificial Intelligence (CoEAI), IIT Kharagpur

Established in 2018, the AI and ML Innovations Centre at IIT Kharagpur, leverages the drive to transform change across industries and society through the power of AI. The efforts are concentrated in four pivotal domains: pioneering research, advanced education, collaborative industry endeavours, and vibrant entrepreneurship.

The academic offerings include PhD, MTech, Dual Degree (MTech), and micro-specialisation programs, providing a comprehensive educational landscape for aspiring AI and ML enthusiasts.

7. NV AI Centre and KRIYA, IIT Hyderabad

The AI department at IIT Hyderabad has gained acclaim for its groundbreaking work in AI and research. This recognition extends to its comprehensive educational programs spanning BTech, MTech, and PhD studies in AI. 

A Center for Research and Innovation in AI (क्रिया) has been entrenched to bolster the department’s research activities. This AI क्रिया Center not only provides spaces for collaboration such as seating areas, classrooms, and conference rooms but also hosts a mini-data centre.

Featuring a range of GPU servers, including the advanced NVIDIA DGX1 and DGX2 deep learning supercomputers, the data centre delivers a staggering 250 TFlops of GPU computing power. Such capabilities empower faculty, research personnel, and students to conduct state-of-the-art AI research on-site.

The post 7 Best Data Science and AI Institutes in India – Top IITs 2024 appeared first on AIM.

Andrew Ng’s DeepLearning.AI recently a free short course called Knowledge Graphs for RAG, collaborating with Neo4j, the king of graph databases. The course provides an intermediate-level exploration of knowledge graphs specifically tailored for Retrieval Augmented Generation (RAG). 

The one-hour course, led by instructor Andreas Kollegger, who handles developer relations for Generative AI at Neo4j, is free for a limited time.

What will you learn? 

Participants will learn to use Neo4j’s Cypher query language to manage and retrieve data stored within knowledge graphs. 

Moreover, they will learn the fundamentals of knowledge graph data storage, employing nodes to signify entities and edges to denote interrelations. Practical exercises entail using Cypher to extract data from a sample movie and actor graph. Additionally, attendees will learn to integrate vector indexing for unstructured text data, enabling efficient text retrieval based on similarity metrics.

Through practical exercises, they will develop skills in crafting queries that manipulate text data to furnish more pertinent context for LLMs in RAG applications. 

They will also build a question-answering system employing Neo4j and LangChain, enabling interaction with a knowledge graph comprising structured text documents.

This course is recommended for individuals seeking to comprehend knowledge graph mechanics, construct RAG applications, and amplify their data analytics capabilities. It will be an added advantage if you are already familiar with LangChain or have completed the course “LangChain: Chat with Your Data”.

The curriculum throws light on the importance of knowledge graphs in organising intricate data relationships, facilitating intelligent search capabilities, and empowering AI applications to reason across diverse data formats. 

Unlike conventional databases, knowledge graphs adeptly capture contextual nuances, enabling the uncovering of intricate insights and connections.

Databases or data structure servers form the backbone of generative AI, powering its capabilities. Amidst this revolution, Neo4j takes a strategic approach, playing the long game – i.e. building trust in generative AI.  “We provide fuel for generative AI companies in the form of high-quality, structured graph data,” said Dr Jim Webber, chief data scientist, Neo4j, told AIM at their Annual Graph Summit 2023. 

Neo4j said it provides graph data on which large language models can be trained. Over time, knowledge graphs have become vital for organising and accessing enterprise data across industries. Today, Neo4j is pivotal in helping enterprises integrate LLMs to enhance data handling. They’re focusing on two use cases: developing a natural language interface for knowledge graphs and creating knowledge graphs from unstructured data.

The post Andrew Ng Unveils Free Course on Knowledge Graphs for RAG with Neo4j appeared first on AIM.

Andrew Ng has come up with a new, free, one-hour course with Hugging Face called “Open Source Models with Hugging Face” which introduces beginners to open-source models. Led by Hugging Face engineers Maria Khalusova, Marc Sun, and Younes Belkada (ML engineer at Hugging Face), the course focuses on leveraging Hugging Face Hub to find and filter open-source models based on task, rankings, and memory requirements.

Participants will learn to write concise code using the Transformers library for text, audio, image, and multimodal tasks. The course emphasises practical applications, allowing users to share their AI apps effortlessly through a user-friendly interface or API, using Gradio and Hugging Face Spaces for cloud execution.

What will you learn?

Throughout the course, attendees will gain the skills to select and employ open-source models from Hugging Face Hub for NLP, audio processing, image analysis, and multimodal tasks. 

The primary learnings include creating a chatbot capable of multi-turn conversations, language translation, document summarisation, text similarity measurement, automatic speech recognition (ASR), text-to-speech (TTS), audio classification without fine-tuning, audio narration generation for images, zero-shot image segmentation, visual question answering, image search, image captioning, and other multimodal applications.

Read more: Data Science Hiring Process at Ramco Systems

The post Hugging Face Introduces New Course with Andrew Ng appeared first on AIM.

A recent addition to the DeepLearning.AI course catalogue is Meta’s “Engineering with Llama 2.” This course allows you to explore prompt engineering using the company’s Llama 2 models. The course is tailored for beginners, requiring just one hour. Led by instructor Amit Sangani, senior director of partner engineering, Meta, the course is currently available for free for a limited time.

You can sign up for the course here.

Participants will gain insights into the best practices associated with prompting Llama 2 models, focusing on practical applications. The curriculum encourages interaction with three key models: Meta Llama 2 Chat, Code Llama, and Llama Guard. These models serve distinct purposes, ranging from conversation and coding assistance to content moderation through Llama Guard.

The primary learning objectives of the course involve familiarizing oneself with the Llama 2 collection, adopting best practices in prompt engineering, and building applications using these models. Through a simple API call, participants will explore the diverse outputs of the Llama 2 models, gaining a nuanced understanding of their capabilities.

The course instructs participants on leveraging Llama 2 models as personal assistants, providing guidance for day-to-day tasks. Additionally, it delves into advanced prompt engineering techniques such as few-shot prompting for sentiment analysis and chain-of-thought prompting for logical problem-solving. Code Llama is introduced as a collaborative partner for pair programming, facilitating both learning and code improvement.

The course also throws light on the responsible use of LLMs by incorporating Llama Guard, which screens user prompts and model responses for potentially harmful content. Importantly, participants are informed that Llama 2 models and their weights are available for free download, including quantized versions for local machine deployment. The course also encourages involvement in an active open-source community that utilizes Llama 2 for building diverse applications.

The post Andrew Ng Unveils New Free Course on Llama 2 with Meta appeared first on AIM.

Today, AI tools have invaded practically every field, including art and design. Learning to use these tools has become more than necessary since they open up unknown possibilities for creative expression. 

Started as an experiment, these tools have now become part of a designer’s daily toolkits. There are many, many tools available in the market, from Midjourney to Firefly. Here are six courses to learn AI image generation for people starting from scratch or looking to learn advanced skills.

Midjourney & ChatGPT: Unleash AI for Unique Image Generation

This easy-to-follow course has everything you need to make the most of Midjourney through these step-by-step lessons:

• Understand Midjourney and its unique features
• Learn how to use settings, suggestions, and advanced choices for creating custom images
• Plan out a business idea using Midjourney, including product pics, photos, website thoughts, and logos
• Get tips and tricks for making super practical suggestions for image creation
• Mix and match ideas to create pictures that are special and nice to look at

Click here to know more.

Make AI Work for You: Break Creative Block

Join this class with Smitesh Mistry, an illustrator, content creator, and Skillshare’s top teacher, where he delves into collaborating with ChatGPT. Here’s what you’ll explore:

• Crafting ChatGPT art prompts tailored to your style and subject
• Sketching thumbnails inspired by AI-suggested concepts
• Utilising Adobe Firefly for visual references in your final illustration
• Translating co-created ideas from AI into your initial sketch

As a bonus, Smitesh provides downloadable ChatGPT art prompts for those still navigating the nuances of communicating with AI software. This class not only guides you in incorporating AI into your drawing process but also imparts fundamental insights for creating illustrations in ProCreate.

Click here to know more.

Adobe Firefly Complete Guide: Learn to Use AI in Projects

Dive into the bestselling ‘Mastering Adobe Firefly’ course on Udemy, designed to help you understand Firefly, Adobe’s AI-powered graphic design tool.

Here’s what you’ll learn:

• Turn simple text into images, making your designs more interesting
• Text Effects: Play around with text using various effects to make ordinary words look amazing
• Learn how to change the colours of vector images precisely so your designs can match any colour scheme
• Explore the cool features of generative fill to create unique and vibrant backgrounds for your designs.

Click here to know more.

10 in 1 Course: Text to Image AI Art Generators Masterclass

This course is your ticket to the secrets of AI tools that turn text into images. It’s not just a simple stroll through various websites and tools; it’s a dive into the art of generating images using AI through creative prompts and tweaking the results to fit your fancy. 

The package covers a range of tools like Bing, Canva, Microsoft Designer, Blue Willow, Leonardo, and more. And here’s the cherry on top – once you are well-equipped with this technology, you’ll walk away with a certificate.

Click here to know more.

Designing with Adobe Firefly and Photoshop

The free 8-hour stream on YouTube provides the perfect overview of Firefly and Photoshop’s newest feature, Generative Fill, with Adobe’s designers and Creative Cloud evangelists. 

The tool has become essential even for traditional designers since the entire Adobe Creative Cloud has been integrated with the tech. Moreover, if you plan to continue using Adobe’s products, getting an idea about how its AI-enabled tools work is a plus.

Click here to know more. 

Making Art with Stable Diffusion

This course is for anyone eager to whip up artwork through AI without any hassle. Picture this: you’ve got projects with deadlines, no time or cash to spare on creating graphics from scratch and zero artistic background. What’s the solution? AI, my friend! It’s quicker, cheaper, and more accessible – it can outdo your solo efforts.

Here’s the lowdown on what you’ll learn during the course:

• Art of generating diverse styles using AI
• Get your hands on customising images with text prompts and variations
• Learn the secrets of Inpainting and Out Painting in AI art
• Master infinite zoom animations
• Creating videos with AI in Stable Diffusion
• Amp up your AI videos with post-production effects
• Discover how Stable Diffusion can work right inside Photoshop

Click here to know more.

The post 6 Courses for Designers & Illustrators to Learn AI Art appeared first on AIM.

Language models managed to woo everyone in 2023. Moving forward, the models will hopefully start looking at the world to understand it better. Making this possible would be computer vision – a technique that can be used in various fields effectively from trying to make cars drive autonomously to detecting cancer. 

Through computer vision, machines can derive information from visual inputs and then act or recommend on that. You can get started and learn advanced computer vision through several courses and resource materials, but most of them can be expensive. Here are 6 free beginners, intermediate and advanced courses on computer vision:

Computer Vision Essentials

The Great Learning course is about image processing knowledge and getting hands-on with the OpenCV library using Python for AI and machine learning. Forget the boring theory, the course provides real-world action – sampling data, messing with images, and learning the ropes of computer vision. 

Instead of stopping at the basics, the course tackles everything from spotting things in pictures to figuring out what’s in computer vision. The last module will be an in-depth discussion of transfer learning. 

Click here to apply.

Introduction to Computer Vision and Image Processing

IBM is offering a beginner-level course on computer vision instructed by Aije Egwaikhide and Joseph Santarcangelo. The course covers various topics such as computer vision applications across different industries, image processing and analysis techniques, Python, Pillow, and OpenCV for basic image processing, image classification, and object detection. 

The course also teaches supervised learning techniques to create an image classifier. Aije Egwaikhide and Joseph Santarcangelo will collectively instruct the subject matter programme. 

Click here to apply.

Advanced Computer Vision with TensorFlow

The course is designed for software and machine learning engineers to learn advanced TensorFlow features. The course covers image classification, image segmentation, object localisation, and object detection. Learners will apply transfer learning to object localisation and detection, customise existing models, and build their models to detect, localise, and label their rubber duck images. 

They will also implement image segmentation using variations of the fully convolutional network (FCN), including U-Net and Mask-RCNN, to identify and detect numbers, pets, zombies, and more. Experts instruct the course in the field, which is designed for early and mid-career engineers with a basic understanding of TensorFlow.

Click here to apply.

Computer Vision with Embedded Machine Learning

The intermediate-level course, offered by a partnership among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation, teaches about neural networks, focusing on the classification of images and object detection in images and videos. One would also learn to deploy models to embedded systems, a field known as embedded machine learning or TinyML.

Throughout the course, students will understand how convolutional neural networks (CNNs) function and how to utilise them for image classification and object detection. The hands-on projects will provide valuable experience training custom CNNs, besides deploying them to microcontrollers and single-board computers.

Click here to apply.

Self-Driving Cars Specialisation

With over 70,000 learners already enrolled, the course offers an understanding of the engineering practices employed in the self-driving car industry. Through hands-on projects using the open-source simulator CARLA, participants will engage with real data sets from an autonomous vehicle (AV).

Throughout the program, students will get insights shared by experts from Oxbotica and Zoox. The course provides a realistic driving environment featuring 3D pedestrian modelling and various environmental conditions. Upon completion, participants will be equipped to develop their self-driving software stack.

Note that specific hardware and software specifications are necessary to effectively run the CARLA simulator, including Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), a quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers). 

Click here to apply.

Computer Vision with OpenCV Python

The OpenCV for Beginners course offers an experiential approach to computer vision, focusing on object tracking, augmented reality, face detection, optical flow, and human pose estimation. Unlike many other courses, this program is tailored to be more intuitive, making it accessible to beginners. 

Upon completion, participants will get a digital certificate from OpenCV.org. The course is part of the OpenCV University, and equips participants with the foundational knowledge needed to pursue further studies in computer vision, deep learning, and AI.

Click here to apply.

The post 6 Free Computer Vision Courses Online appeared first on AIM.

SQL is a super helpful, universally applicable language for accessing and manipulating databases. The tool is widely applied in careers such as business analytics, data science, software engineering, and journalism. 

Getting the hang of SQL can help get job opportunities and career advancements, thanks to its prevalence across professional sectors, user-friendly nature, and the robustness of its functions.

From the many courses available on SQL over the internet, here is a curated list of six free ones to get you started and level up your SQL game.

Best SQL Certification Course for Free

Learn SQL

Through this course, Codecademy focuses on managing large datasets and analyzing real data through the standard language for data management. Taken by 1,200,176 learners across four sessions, this beginner-level program delves deep into the curriculum. 

It equips participants with the skills to navigate SQL and apply them to manipulate databases effectively, providing a robust foundation for handling substantial datasets in real-world scenarios.

Click here to apply.

SQL Projects for Beginners

Presented on My Great Learning, this course aims to provide practical knowledge in managing and querying databases through DBMS tools. Focused on SQL, the first half introduces Database Management Systems. It then delves into understanding the Relational Database Management System, a critical interface managing data storage and performance.

The latter part offers a hands-on SQL project for beginners, complete with source code, fostering practical experience. Participants can wrap up the course by taking a quiz and obtaining a certificate, solidifying their understanding of DBMS tools and SQL applications.

Click here to apply.

SQL for Data Science

Starting with exploring SQL clauses, the program progresses to cover essential statements like GROUP BY, ALIAS, and various types of JOINS (INNER, LEFT, RIGHT, FULL, and SELF). Subsequent sections guide you through working with subqueries, understanding Python concepts in relation to SQL, and its overall popularity. 

The course includes:

• A Python installation guide
• Assessments for skill evaluation
• The opportunity to obtain a certificate upon completion

Refer to the attached materials for additional reference to explore the world of SQL and Python for data science at no cost.

Click here to apply.

Intermediate SQL Server

The course by DataCamp is all about using T-SQL, a kind of SQL for Microsoft SQL Server, to analyze and clean up data right in the databases where it’s stored. You’ll learn practical stuff like dealing with missing data, working with dates, and using advanced queries to calculate summary stats.

Once you finish the course, you’ll have the skills to analyze data quickly and easily, drawing insights from it. It’s like getting the key to unlock useful info from databases using T-SQL – perfect for anyone dealing with data in Microsoft SQL Server!

Click here to apply.

Reporting in SQL

Learn to create impactful SQL reports and dashboards while refining data cleaning and validation skills in this course. Apply previously learned SQL concepts and functions to construct a personalized dashboard using an Olympics database. 

You can also explore data, manipulate, clean, validate, and perform complex calculations to enhance your practical SQL skills. Build your dashboard and gain hands-on experience with real-world data scenarios.

Click here to apply.

Advanced Databases and SQL Querying

This Udemy course is for those who already know the basics of databases and can handle simple TSQL queries. If you’ve taken the instructor’s previous course, even better! In about 2+ hours, you’ll dive into advanced stuff like Views, Triggers, and Dynamic Queries. Each concept comes with assignments to help you practice.

It’s great if you’re eyeing a database job or want to boost your database skills and use them in your projects. It’s the next step to leveling up your TSQL game. 

Click here to apply.

Resources

• 5 Free NoSQL Database Certification
• Specialised Courses by Andrew NG
• Free Generative AI Courses
• Free Resources To Learn Tableau
• Best Courses on Vector Database

The post Best SQL Certification Courses for Free in 2024 appeared first on AIM.

A few days ago, Meta AI introduced ‘Prompt Engineering with Llama 2‘, a new resource created for the open source community, which is a repository for the best practices for prompt engineering. Even Andrew Ng’s DeepLearning.AI recently released a course on this, called Prompt Engineering for Open Source LLMs. IBM, Amazon, Google, and Microsoft, have all been offering similar courses on prompt engineering for open-source models. 

Prompt engineering was one of the most talked about professions in 2023. As companies adopted OpenAI’s ChatGPT in different ways, they also got busy hiring experts who could prompt the chatbot to elicit the right responses, allegedly paying them huge paychecks. 

This also led to the rise of hundreds of prompt engineering courses that everyone wanted to get their hands on. But most of these were for closed source models such as OpenAI’s. Now, as companies adopt open source LLMs such as Meta’s LLaMA and Mistral, it becomes necessary to understand how prompt engineering is different for open source LLMs.

Several corporate entities are developing and testing customer support and code generation applications based on open source technology. These applications are designed to engage with proprietary code unique to the companies, often proving challenging for the general closed-model LLMs created by OpenAI or Anthropic. 

“A lot of customers are asking themselves: Wait a second, why am I paying for a super large model that knows very little about my business? Could I not use just one of these open-source models, and by the way, maybe use a much smaller, open-source model for that (information retrieval) workflow?” shared Yann LeCun in a post on X.

Prompt engineering for open source? 

Recently, Sharon Zhou, co-founder and CEO of Lamini, in partnership with DeepLearning.AI, conducted a course for prompt engineering on open source LLMs. She highlighted how the packaging of an open source model is different from the closed one, which affects the API, in the end, affecting the prompting mechanism. 

“LLMs wear pants, which is its prompt setting,” said Zhou, drawing a crazy analogy to how everyday people come to office wearing pants, and how that is the correct decision to wear them, and a change in this affects the whole system. 

She said that a lot of people get confused between prompt engineering, RAG, and fine-tuning. “Prompting is not software engineering, it’s close to Googling,” she added, and also spoke at length about this in her post on X recently. She added that RAG is prompt-engineering, “do not overcomplicate it”, it is just about retrieving information.

Zhou emphasised the simplicity of prompt engineering, reiterating that prompts are just strings. She compared the process to handling a string in a programming language, making it clear that it’s a fundamental skill that doesn’t require complex frameworks. “Different LLMs & LLM versions mean different prompts,” she added. 

However, she acknowledged that many frameworks tend to overcomplicate prompt engineering, potentially leading to suboptimal results. Zhou explained that in practice, it’s essential to tailor your prompts when transitioning between different LLMs. This is similar to when OpenAI undergoes version changes, leading to confusion when previously effective prompts no longer yield the desired results. 

The same is the case with open source LLMs. Maintaining transparency in the entire prompt is crucial for optimising the model’s performance. Many frameworks face challenges in this regard, often attempting to abstract or conceal prompt details, creating an illusion of managing processes behind the scenes. 

Hits and misses 

When it comes to enterprise adoption, Matt Baker, the SVP of AI strategy at Dell, the company which partnered with Meta for bringing open source Llama 2 to enterprise use cases, said that large models are of no use for companies unless they are made for specific use cases. This is where smaller, specialised, and fine-tuned models come into the picture, giving birth to RAG and prompt engineering. 

Though the reality is that most of the companies would be using open and closed source LLMs for different use cases, the majority of information retrieval is now dependent on APIs and open source models, fine-tuned with their data, which is why companies need to adapt to learn how to prompt models precisely and give accurate information.

To put it in Zhou’s words, always put the right pants on!

The post Prompt Engineering is Different for Open Source LLMs appeared first on AIM.

NoSQL databases diverge from traditional relational databases by leveraging non-tabular storage methods, such as document databases tailored for storing data in document formats like JSON and XML. Some of the types of NoSQL databases include key-value stores, wide-column databases, graph databases and more. MongoDB, Cassandra, HBase, Hypertable, and Neo4j are a few big names in this space.

AIM has curated a list of the top five free courses that every developer can take up to understand and implement NoSQL databases. 

1. Database Architecture, Scale, and NoSQL with Elasticsearch

The University of Michigan offers a course on database architecture, scale, and NoSQL with Elasticsearch as part of PostgreSQL. Taught in English, but available in over 20 languages, the course covers PostgreSQL architecture, SQL and NoSQL analysis, and a comparison between ACID and BASE style architectures.

The intermediate-level free course requires approximately 10 hours to complete, offering flexibility in scheduling. The learning experience involves assessments through two quizzes, and a shareable career certificate. The final module explores database architecture, PostgreSQL, and scalable deployment configurations, emphasising basic CRUD operations, indexes, and the implementation of ACID requirements.

Register Database Architecture, Scale, and NoSQL with Elasticsearch. 

2. Introduction to NoSQL Databases

With over 25 years of industry experience, Sarma Pydipally, principal engineer (database) of Verizon Data Services, brings to you this free introductory course on NoSQL databases. 

This training course provides an introduction to the evolution of database technology, specifically focusing on the transition from traditional DBMS to NoSQL databases. The course covers the history and development of NoSQL databases, exploring concepts such as vertical scaling vs horizontal scaling and the scaling challenges faced by previous generations. Additionally, it delves into the Five V’s of big data – velocity, volume, variety, veracity, and value.

The need for NoSQL databases and their promises are examined, along with a discussion on the varied types within the NoSQL landscape. The course addresses document databases, key-value stores, graph databases, wide column stores, and time-series data. 

Find out more about the course on the Introduction to NoSQL Databases. 

3. NoSQL, Big Data, and Spark Foundations Specialisation

Available for free, the IBM NoSQL, Big Data, and Spark Foundations Specialisation is a three-course series designed to equip individuals with practical skills for a career in big data. The specialisation, taught in English by the IBM Skills Network Team, covers essential concepts in NoSQL databases, Big Data technologies, and Apache Spark. 

Participants gain hands-on experience working with popular NoSQL databases like MongoDB, Apache Cassandra, and IBM Cloudant, mastering tasks such as data insertion, updating, querying, and indexing. The curriculum also delves into foundational Big Data knowledge, including Apache Hadoop, MapReduce, and Kubernetes, with a focus on hands-on lab experience. 

Participants learn extract, transform, and load (ETL) processing and machine learning model deployment using Apache Spark, Spark SQL, and SparkML. The specialisation is suitable for beginners and targets aspiring data engineers, software developers, IT architects, data scientists, and IT managers. 

Sign up for the course NoSQL, Big Data, and Spark Foundations Specialisation 

4. NoSQL and DBaaS 101

This course by IBM, NoSQL and DBaaS 101 provides an overview of the NoSQL database landscape, emphasising the advantages of utilising Database-as-a-Service (DBaaS) and introducing the role of IBM Cloudant within this context. The course also covers account setup, database creation and replication, data loading and querying using Cloudant. 

The objective is to equip participants with the necessary skills and understanding to leverage a flexible schema for fast application development, handle scalable datasets and concurrent users, and explore the benefits of offloading database administration to a service provider. 

The prerequisites for the course include a connected laptop or desktop, a modern browser (preferably Google Chrome), and the installation of cURL for accessing HTTP REST API from the command line. Prior knowledge in databases, HTTP, JSON, and basic familiarity with browsers and cURL on the Linux command line is recommended.

Register NoSQL and DBaaS 101 Course by cognitiveclass 

5. MongoDB Aggregation Framework

Learn MongoDB Aggregation Framework through this free course by the database giant itself. It holds an intermediate level, requires no prior experience and can be completed in 18 hours over three weeks, with a flexible schedule allowing you to learn at your own pace. 

The curriculum covers schema design, relational data migrations, and machine learning with MongoDB. Upon completion, you’ll possess the skills to effectively use MongoDB and its aggregation framework in your data science workflow. 

Find more information MongoDB Agreegation Framework by Coursera

Related Reads

1. Most Popular NoSQL Database
2. NoSQL vs SQL – Which is better?

The post 5 Free NoSQL Database Certification Courses in 2024 appeared first on AIM.

Vector databases form the backbone of generative AI, using embeddings to capture data meaning and assess vector similarities. They are vital in fields like natural language processing, image recognition, recommender systems, and semantic search. With the rising use of LLMs, vector databases offer real-time proprietary data access, facilitating the creation of Retrieval Augmented Generation (RAG) applications.

Given the importance of vector databases, we have curated a set of courses for your learning journey. 

Vector Databases: From Embeddings to Applications

Andrew Ng-led DeepLearning.AI’s ‘Vector Databases: From Embeddings to Applications‘ is a free course on vector databases, their applications, and how they can be used to develop generative AI applications without training or fine-tuning an LLM. The course aims to help learners gain the knowledge to make informed decisions about when to apply vector databases to their applications. 

The key topics in this course include, using vector databases and LLMs to gain deeper insights into data, building labs that demonstrate how to form embeddings using various search techniques to find similar embeddings and exploring algorithms for fast searches through vast datasets and building applications ranging from RAG to multilingual search.

Master Vector Database with Python for AI & LLM Use Cases

In this course by Udemy, you will learn to work with Vector Databases using Python with a focus on AI and LLM applications. The course covers techniques for vector data embedding, indexing, and retrieval, including practical exercises with semantic search and named entity recognition. You will also explore Pinecone vector database, LangChain, and transformer models for vector embedding, along with generative AI and OpenAI API usage. 

At INR 449, the course provides insights into the fundamentals of vector databases and their role in AI workflows, enabling similarity search and nearest neighbour retrieval. This course is suitable for data professionals, AI researchers, machine learning engineers, and anyone with a technical background interested in cutting-edge AI technologies. 

A basic understanding of programming concepts and proficiency in at least one language, like Python or Java, is essential. Additionally, an understanding of data analysis and machine learning, familiarity with databases, encompassing tables, queries, data manipulation principles, and knowledge of NumPy and Pandas for data manipulation is advantageous. 

Learn Embeddings and Vector Database

The curriculum covers understanding and creating embeddings using vector databases, and implementing advanced AI solutions. The skills one can learn from this free course include proficiency in AI, online databases, AI development, AIOps (AI for IT Operations), and AI systems. With a flexible schedule and an intermediate level, this subscription-based two-hour course requires no previous experience and is designed for self-paced learning.

The course focuses on embeddings and their role in interpretative processes, covering practical exercises with tools like Supabase. Participants engage in challenges in text pairing, semantic searches, and similarity searches, mastering tasks such as creating conversational responses with OpenAI and handling text chunking.

The course combines theoretical understanding with practical skills, ensuring learners not only understand the technical aspects but also develop a proof of concept for an AI chatbot, prepared for real-world challenges.

Master Vector Databases

This course, priced at INR 449, includes seven hours of on-demand video, five articles, and 12 other resources. The instructor emphasises practical learning through code-along exercises, providing skills to build and optimise vector indexing systems for real-world applications. 

It explores the fundamentals of vector databases and their applications in AI, generative AI, and language models. Topics covered include vector basics, embedding techniques, SQLite as a Vector Database, ChromaDB, Pinecone DB, Qdrant Vector Database, and applications of LangChain and OpenAI Embeddings. 

LangChain & Vector Databases in Production

Gen AI 360, a collaboration between Activeloop, Towards AI, and Intel Disruptor Initiative, provides foundational model certification for generative AI professionals, executives, and enthusiasts. This free certification is a comprehensive three-course series that provides essential skills for mastering LLMs, covering everything from training to implementation in production.

The first course, featuring over 50 lessons and 10 practical projects, focuses on LangChain and introduces Deep Lake, a cutting-edge vector database for AI data. It also covers prompt engineering, knowledge organisation with indexes, and building applications such as automated sales agents and recommendation engines.

The course is designed for individuals with intermediate Python knowledge, basic understanding of Jupyter Notebooks, and familiarity with GitHub. 

Read more: How Redis Finds Moat in the Indian Market

The post 5 Best Courses on Vector Database  appeared first on AIM.

The past two weeks have been exceptionally crazy in terms of the new AI updates pouring in. We decided to curate the top six frameworks and models that were released recently.

ActAnywhere: Subject-Aware Video Background Generation

Adobe Research and Stanford University have introduced Act Anywhere, a generative model addressing the challenge of aligning video backgrounds with foreground subject motion in the film industry and visual effects field. This model automates the typically labour-intensive process by leveraging large-scale video diffusion models. 

It takes a sequence of foreground subject segmentation and a condition frame describing the desired scene as input, producing a realistic video with coherent foreground-background interactions. 

Trained on a large-scale dataset of human-scene interaction videos, results show that ActAnywhere performs well compared to baselines and proves its ability to handle diverse out-of-distribution samples, including non-human subjects.

GALA

Meta has always tried to make its avatars better across its different platforms like Facebook, Instagram and WhatsApp. So, Codec Avatars Lab of Meta collaborated with Seoul National University to introduce GALA, a framework that converts a single-layer clothed 3D human mesh into fully-layered 3D assets, allowing the creation of diverse clothed human avatars in various poses. 

Unlike existing methods that treat clothed humans as a single-layer geometry, GALA is based on the compositionality of humans with hairstyles, clothing, and accessories, enhancing downstream applications. Decomposing the mesh into separate layers is challenging due to occlusions, and even with successful decomposition, the poses and body shapes are often not real-life-like. 

To overcome this, the researchers used a pre-trained 2D diffusion model as a prior for geometry and appearance. The process involves segmenting the input mesh using 3D surface segmentation from multi-view 2D segmentations, synthesising missing geometry in both posed and canonical spaces with a new pose-guided Score Distillation Sampling (SDS) loss, and applying the same SDS loss to texture for complete appearance. This results in multiple layers of 3D assets in a shared canonical space, normalised for poses and human shapes, facilitating easy composition of novel identities and poses.

Lumiere

In an effort to address the challenge of creating realistic, diverse, and coherent motion in synthesised videos, Google has come up with Lumiere, a text-to-video model, made in partnership with Weizmann Institute, Tel-Aviv University and Technion. The training involved a Space-Time U-Net architecture, which generates the entire video duration in one go, unlike existing models that use distant keyframes and temporal super-resolution. 

By combining spatial and temporal processing and leveraging a pre-trained text-to-image model, the system directly produces full-frame-rate, low-resolution videos. It excels in text-to-video tasks, like image-to-video and stylised generation. The model demonstrates state-of-the-art text-to-video results and is versatile for tasks like image-to-video, video inpainting, and stylised generation. 

However, it currently cannot handle videos with multiple shots or scene transitions, and further research is needed in those areas. Despite some limitations, the focus of this project is on empowering users to creatively and flexibly generate visual content.

Meta-Prompting

In yet another interesting research paper, OpenAI and Stanford University teamed up to present meta-prompting, an effective scaffolding technique to enhance language models (LMs) performance in a task-agnostic manner. This is done by turning them into versatile conductors that can manage multiple independent queries. Meta-prompting is task-agnostic, simplifying user interaction without requiring detailed instructions.

Experiments with GPT-4 show the superiority of meta-prompting over traditional methods, achieving a 17.1% improvement over standard prompting, 17.3% over dynamic prompting, and 15.2% over multi-persona prompting across tasks like the Game of 24, Checkmate-in-One, and Python Programming Puzzles.

Using clear instructions, meta-prompting guides the LM to break down complex tasks into smaller subtasks which are then handled by specialised instances of the same LM, each following tailored instructions. The LM acts as a conductor, ensuring smooth communication and effective integration of outputs. It also leverages critical thinking and verification processes to refine the results. This collaborative prompting allows a single LM to act as an orchestrator and a panel of experts, improving performance across various tasks.

Self-Rewarding Language Models

In a recent research paper by Meta and NYU, self-rewarding language models have been introduced which do not rely on reward models derived from human preferences, which may be limited by human performance and cannot improve during training. These models can align themselves by evaluating and training on their outputs and use the language model itself to generate rewards through LLM-as-a-Judge prompting.

The method involves iterative training, where the model generates its preference-based instruction data by assigning rewards to its own outputs using LLM-as-a-Judge prompting. The results show that this training improves the model’s ability to follow instructions and improves its reward-modelling across iterations. 

Gaussian Adaptive Attention is All You Need

This study introduces the Multi-Head Gaussian Adaptive Attention Mechanism (GAAM) and the Gaussian Adaptive Transformer (GAT) to improve model performance and contextual representation, especially with highly variable data. GAAM incorporates learnable mean and variance into its attention mechanism, structured within a Multi-Headed framework. This setup allows GAAM to collectively represent any Probability Distribution, enabling the ongoing adjustment of the importance of features as needed.

The study also introduces the Importance Factor (IF) for enhanced model explainability. GAAM, a new probabilistic attention framework, and GAT are proposed to facilitate information compilation across speech, text, and vision modalities. It surpasses state-of-the-art attention techniques in model performance by identifying key elements within the feature space. 

The paper has been published by the James Silberrad Brown Center for Artificial Intelligence, Carnegie Mellon University, Stanford University and Amazon. 

The post Top 6 Recent Updates that Will Transform the Course of AI appeared first on AIM.

The open source small language models are increasingly competing with their larger counterparts. The likes of 7-13 billion parameter models such as Meta’s LLaMA, Microsoft’s Phi-2, and Mistral are giving tough competition to OpenAI’s GPT-4 or Google’s Gemini. 

Recently, Maxime Labonne, a researcher and open source enthusiast, released Phixtral, which combines the competence of Mistral’s Mixture of Experts model and Microsoft’s Phi-2. 

AIM got in touch with Labonne, the creator of Phixtral and NeuralBeagle, to understand his views on the AI landscape, the importance of open source, and smaller language models being on par with larger closed ones. 

“In the long run, open source is [going to be] too powerful and there are way too many people who want to experiment with it right now,” Labonne said. He believes that eventually open source models will overtake the closed ones. “It is like there are monkeys with a typewriter – one of them will create a masterpiece at some point,” he quipped about open source developers, who are not bound by companies and academic institutions.

Sometimes, the dumbest ideas work

Apart from being an open source contributor, Labonne also works at JPMorgan as a machine learning scientist, and is also an avid gamer. Calling himself ‘GPU poor’, Labonne currently juggles between using his computer to play Vampire Survivors and experimenting with open source language models.

Running experiments on his laptop, Labonne said that he is a huge fan of 7 billion parameter models. “I think it’s a sweet spot with a balance between the requirement of compute and the knowledge,” he added that larger models are obviously going to be good at reasoning, but distilling information through quantisation from large models into smaller models is something that he bets on.

“Currently, all the benchmarks that we have are bad,” laughed Labonne, highlighting that the two ways of benchmarking, one being automated and the other manual ones such as MMLU and HumanEVAL, are both problematic as they can be easily altered by fine-tuning on testing dataset. 

Even then, he said that it is the best way that we have currently. “If you go to the leaderboard that I have created and pick the top model, it actually performs better than the rest,” Labonne said. He has built LLM-autoeval, a Google Colab where developers can just specify the name of the model, select a GPU, and run the tests.

Apart from this, Labonne has also created an LLM course with roadmaps and divided it into three parts – LLM fundamentals for beginners, LLM scientist for building LLMs, and LLM Engineer for building applications and deploying them. “A lot of people have been asking me from different backgrounds about how to get into LLMs, so I created this course for people from every background,” he added. 

Currently, Labonne is working on creating his own benchmark and also a very passionate project called ‘Chess LLM’, where developers can create their own test dataset, train very small language models, and make them compete in an arena. “They are very bad at playing chess right now, and it is very funny to see the leaderboard.”

The best open source model and AGI

There is a group of people in the AI realm – who occasionally identify themselves as so-called AI ethicists – that are concerned with generative AI taking over the internet, and eventually leading to models collapsing when being trained on synthetic data generated by AI models. “I think it’s just the opposite,” Labonned replied, saying that synthetic data is actually proving to be a lot more effective than initially thought. 

“Now we have solid evidence to prove that synthetic data is of really high quality and it can’t ruin the internet,” Labonne gave examples of models such as Orca, that is trained on GPT-4 data and actually performs a lot better than others. 

Labonne has been experimenting with a lot of open source models such as Mistral, LLaMA, Phi-2, Falcon, and models from China such as DeepSeek. According to him, Falcon was great when it came out and was on top of the charts, but now models such as Mistal and Llama 2 are outperforming everyone. 

“Phi-2 is great, but I think Mistral’s models are the best right now,” he said, and added that Chinese models are not suitable for English use cases at all. 

Labonne says that OpenAI’s strategy of going closed door since GPT-3 is good for the company, but Meta’s open source approach is something that makes him really happy. “I am not very interested in the AGI conversation, I don’t even see how it’s possible right now,” he laughed and wished the companies striving for it, the best of luck. 

“We will talk about AGI after 10 years,” he concluded, adding the LLMs are one of the most powerful tools, but we need something stronger to achieve that, and it is not cost effective to keep training bigger models such as GPT-5, as we have reached almost a saturation point.

The post Open Source Developers are Monkeys with Typewriters appeared first on AIM.

Andrew Ng’s DeepLearning.AI has come up with a free short course, LLMOps, offered in collaboration with Google Cloud. The course is designed in a way that beginners find it easy to learn in one hour of instruction by Erwin Huizenga, a Machine Learning Technical Lead at Google. The target audience includes individuals interested in tuning LLMs and building LLMOps pipelines.

Participants will gain practical knowledge on adapting an open-source pipeline to implement supervised fine-tuning on an LLM for better user question responses. The course emphasises best practices, such as data and model versioning, and covers the pre-processing of substantial datasets within a data warehouse.

Responsible AI practices are also addressed, focusing on the output of safety scores for sub-categories of potentially harmful content. They will also delve into the LLMOps pipeline, learning to retrieve and transform training data, version data and tuned models, configure an open-source supervised tuning pipeline, and deploy a tuned LLM. 

The course comes with practical applications which include creating a customised question-answer chatbot, like one for Python coding queries. BigQuery data warehouse, open-source Kubeflow Pipelines, and Google Cloud are some of the tools taught in this course. 

Ng had earlier partnered with Google Cloud for the course ‘Understanding and Applying Text Embeddings with Vertex AI’ which focussed on leveraging text embeddings to capture the essence of sentences and paragraphs. 

Read more: How SAP Joule is Quenching Enterprises’ GenAI Thirst

The post Andrew Ng Partners with Google Cloud for New Course on LLMOps appeared first on AIM.

With large language models (LLMs), a whole new wave of generative AI turned the game upside down in 2023. The new ecosystem of techniques, tools, and vendors has even left AI and machine learning veterans scrambling to wrap their heads around the possibilities and figure out the right resources.

While the community continues to experiment and explore LLMs, here are six good courses to learn everything about these language models.

Full Stack LLM Bootcamp

DeepLearning AI conducts the LLM course personally recommended by AI genius Andrej Karpathy. The two-day program is based on the current best practices and the research results to help generative AI developers make the transition to building applications around language models with confidence.

The instructors ran the program as an in-person boot camp in San Francisco in April 2023. Now, they have released the recorded lectures for free. The course’s objective is to teach regular Python coders how to build applications of LLMs. Instead of the old-school way of building apps with pre-trained models, APIs can be configured and functioning in an hour.

Generative AI with Large Language Models

The free course on Coursera is like an AI 101 as it covers the basics and practical stuff for an in-depth understanding of how these generative AI models actually work. The instructors, Antje Barth, Shelbee Eigenbrode, Mike Chambers, and Chris Fregly, will break down the latest research and show you how companies are actively building LLMs.

The course, provided by DeepLearning AI and AWS, already has 166,689 enrolments.

Generative AI Fundamentals

Software leader Databricks has actively contributed to the language model landscape over the past year. Apart from building and open-sourcing its own language models, the company also started dishing out free on-demand knowledge on the ABCs of generative AI. 

It’s a four-part video session explaining extensively what you can do with large language models. They’re also making sure you learn about the risks and challenges that come with this tech. The Databricks-style crash course is a great point to start learning about generative AI. 

Hugging Face NLP Course 

Most of the large language models released in the past year have been hosted on Hugging Face. Even models by big tech companies like Meta’s LLaMA are available on the developers’ platform. The platform also hosts the Hugging Face Course that would equip you with the right skills to understand the basic concepts of NLP and language models simultaneously.

Moreover, the course is like a secret stroll through the Hugging Face environment, a backstage pass to the generative AI party.

Introduction to Large Language Models

The course presented by Google Cloud is a quick introductory one that breaks down what LLMs are, where they can be used and how to make them better through prompt tuning. It also covers Google tools to help you develop generative AI applications.

LLM University

Lastly, Cohere’s LLM University offers a deep dive into NLP techniques. The platform has got you covered in everything from semantic search and generation to classification and embeddings. It’s a one-stop shop with a mix of theory and hands-on exercises, giving you knowledge on a bunch of topics. Whether you’re just starting out or a seasoned pro, this platform will polish your LLM skills.

The post 6 Free Courses on Large Language Models appeared first on AIM.

Coursera launched a large catalog of learning content in Hindi and AI-powered features to make online learning more personalised and interactive. Now, top courses like Generative AI for Everyone from DeepLearning.AI, the Science of Well-Being from Yale University, Programming for Everybody from the University of Michigan, and What is Data Science? from IBM, which has until recently been available only in English, are going to be accessible to anyone who speaks Hindi.

Coursera also announced new enterprise and campus customers as institutions across the country embrace online learning to equip their employees and students with digital skills. With over 23.4 million learners and 57 million enrollments on the platform, India represents the 2nd largest market for Coursera globally.

“India’s ambition to become a USD 5 trillion economy depends on its ability to develop a skilled workforce and maximize its demographic dividend. Our goal is to make high-quality education available to everyone, no matter what language they speak, and today marks a big step towards that goal. We have used the power of AI to translate over 4,000 courses into Hindi, giving learners in India unprecedented access and flexibility to develop skills for the digital future,” Jeff Maggioncalda, CEO at Coursera, said.

Here are the new initiatives and features Coursera is unveiling in India:

Hindi translations: More than 4,000 courses are now available in Hindi, including some of the most popular courses in India, such as Supervised Machine Learning: Regression and Classification from DeepLearning.AI and Stanford, Financial Markets from Yale, and Learning How to Learn from Deep Teaching Solutions. Learners can access course readings, lecture video subtitles, quizzes, assessments, peer review instructions, and discussion prompts — all in the local language. Over 40 courses from top Indian educators like Introduction to Programming from BITS Pilani, Leadership Skills from IIM Ahmedabad, and Trading Basics from Indian School of Business will also be translated into 18 languages, including French, Spanish, German, and Thai, enabling India’s vision to become the global hub for education.

GenAI Academy Launch: Designed to offer foundational literacy and executive education programs from top universities and companies, including Stanford Online, Vanderbilt, DeepLearning.AI, Google Cloud, and AWS. L&T is the first enterprise in India to launch Coursera’s GenAI Academy to provide structured digital literacy across its workforce.

Coursera Coach (beta) for Coursera Plus subscribers: A GenAI-powered virtual learning assistant that provides personalized feedback, answers questions, and summarizes video lectures and resources. Coach will also support learners with interaction in the local language.

Coursera Course Builder: Based on prompts from human authors, the AI-powered course-building tool will auto-generate content, including course structure, descriptions, readings, assignments, and glossaries. Companies and campuses can also use this feature for private authoring, using their internal experts to produce custom courses and blend them with recommended content from participating partners on Coursera.

New and expanded partnerships: 45 Coursera for Business customers, including Aditya Birla Group, Tata Power, KPIT Technologies Ltd, and Bajaj Finserv, bringing the total to 140 enterprise customers in the country. 55 Coursera for Campus customers, including XLRI Jamshedpur, Somaiya Vidyavihar University, Alliance University, and Yenepoya University, bringing the total to 1,100 higher educational institutions in the country.

New programmes from Indian institutions:
Specialisation – Advanced Digital Transformation – from IIM Ahmedabad
Degree – Master of Science in Information Technology – from IIIT Hyderabad

The post Coursera launches new AI features in India, translates 4,000 courses into Hindi appeared first on AIM.

Considering that Java is the most popular and developer favourite programming language, DeepLearning.AI, once again in collaboration with LangChain, has released a new course “Learn how to build LLM applications with LangChain.js”

This free intermediate-level course is of one-hour program and will be led by Jacob Lee, LangChain’s founding engineer. It will cover different concepts in JavaScript for generative AI application development. 

The course will cover various aspects, including using data loaders to extract information from common sources such as PDFs, websites, and databases. It will also delve into the use of prompts to provide context for the LLM, modules supporting RAG like text splitters and integrations with vector stores, working with different models for applications that aren’t vendor-specific, and implementing parsers to extract and format output for downstream code processing. 

Through this comprehensive exploration, participants will gain a practical understanding of building applications with LangChain.js and leveraging its capabilities for effective LLM development.

You’ll also work with the LangChain Expression Language, enabling the easy composition of sequences, or chains, of modules for performing complex tasks with LLMs. This hands-on experience will be applied to the development of a conversational question-answering LLM application that can effectively use external data as context. 

This marks Andrew Ng’s fourth collaboration with LangChain, following the release of the courses “LangChain: Chat With Your Data!”, “LangChain for LLM Application Development,” and “Functions, Tools, and Agents with LangChain”.

The post Andrew Ng Teams Up with LangChain for Another Course on LLM Development appeared first on AIM.

Andrew Ng’s DeepLearning.AI, in collaboration with Chroma, an open-source embedding database company focused on AI-native solutions, has introduced a free one-hour course designed to teach participants advanced retrieval techniques specifically tailored for AI applications.

Called “Advanced Retrieval for AI with Chroma”, this short course is ideal for individuals with intermediate Python skills and a keen interest in mastering advanced retrieval techniques for extracting data from vector databases. 

Led by Chroma cofounder Anton Troynikov, the primary focus is on refining information retrieval processes to ensure that the output from a database query is not only semantically similar but also highly relevant to the query and its intended application. Leveraging an LLM enhances the effectiveness of this traditional technique, and the course explores another form of expansion where the LLM suggests a potential answer to the query, subsequently included in the query itself. 

Additionally, participants will also learn about cross-encoder reranking, a method to reorder retrieval results and prioritise those most relevant to the query, thereby improving overall results. Furthermore, the course covers the training and application of embedding adapters, introducing an adapter layer to reshape embeddings and improve elements pertinent to the specific application, leading to better retrieval outcomes.

Ng, who has democratised AI education for all through his free courses had earlier released courses on various topics like generative AI for all, LLM quality and security, vector database for LLMs and more. He has participated with several companies like Microsoft, Lamini, AWS, OpenAI for these training materials. 

The post Andrew Ng Releases New Course on Advanced Retrieval Techniques for AI Applications appeared first on AIM.

Two years ago, Kyndryl started as a separate company after breaking away from IBM. The reason for the split from the Big Blue was that as a piece the division didn’t fit with CEO Arvind Krishna’s hybrid and AI-focused vision. The company set on its own path to ‘become the technology services employer of choice’.

Today, Kyndryl helps companies modernise by designing and managing IT infrastructure systems involving massive amounts of data. To understand how this American IT infrastructure provider is doing its own thing with automation and tech, especially in India, AIM had a chat with Naveen Kamat the Vice President & CTO of Data and AI Services at their Bangalore office. 

“Most organisations have multiple data stores and data pipelines which causes redundancy over time for various reasons. The same data is replicated leading to multiple copies which is not needed,” he elaborated. A part of their approach is to reduce all this duplication and redundancy. Another aspect is, ‘technical debt’ caused by legacy code that leads to more data incidents and downtime that leads to data breaches. 

“In that context, data becomes more of a liability,” Kamat stated. “Having the right data foundation cuts down the cycle time that goes into data engineering that can be then used across the lines of business,” mentioned the IT leader who has been in the industry for over two decades working across several roles. 

Without disclosing one of Kyndryl’s clients which is a large healthcare enterprise Kamat explained the need for data foundation in terms of pharmacy application. He recalled that major incidents were causing the application to be down. “These are very critical situations, particularly if the patient is in the ICU. So, we worked on the right data foundation to make sure that the various log metrics from various parts of the stack are all ingested into this data lake,” he said. Kamat also mentioned a ballpark figure of nearly 80% reduction in major outages across verticals including healthcare.

A Diverse Clientele

Today, the company boasts of a fairly large client base across industries. “Apart from our skills, experience and resources, we also have been investing into our partner ecosystem, which includes Microsoft, and AWS, apart from data platforms like CloudEra and Databricks. We bring in additional differentiation when we work with clients with our IP, assets and accelerators,” mentioned Kamat. The company also announced banking and financial industry services for Google Cloud customers strengthening its existing partnership with the cloud provider. 

Talking about how Kyndryl works with clients of different tiers, Kamat stated, “From a data foundation standpoint, we engage depending on the client’s maturity. We offer tailor made options to our customers that fit their purpose, be it a solution or architecture. Based on their budgets and business goals, we align the data strategy and data foundation that is the most relevant for them. A lot of times large enterprises make investments in technologies and tools, and we protect that investment, modernise and help them go to the next generation.”

Generative AI 

No conversation around tech has happened without generative AI being mentioned. Commenting on the phenomenon, Kamat mentioned that data is the key element. “With this [generative AI] wave we are seeing innovation on steroids. A lot of agility has come into the ecosystem. A task that would earlier take months to be done is now crunched to a few weeks. The turnaround time is rapid,” he added.

The company is currently focused on a few aspects of generative AI. It is working with its customers by helping them prioritise what use cases can be looked at from a quick win standpoint. “The good thing is that a lot of the hype has settled down now and there is a lot more awareness around the options that are available in terms of large language models,” said Kamat.

“You can use different language models to get the same outcome with more or less the same accuracy, but much less carbon footprint and resource consumption. It is not only sustainable and optimal as a prototype, but also from a longer-term deployment standpoint,” he added.

With great power comes great responsibility. As Kyndryl is helping various clients from pharmaceutical companies to international airports deploy generative AI, it is making sure the digitization is done in the right manner. “Across the organisation, we are ensuring that not only the right policies are established, but those are followed as the standard data stewardship comes into play. Data security and privacy need to be organisation-wide programmes.”

That’s where Kamat recommends data literacy programmes across enterprise for a broader awareness of how data can be used or misused. “You can’t just have a strategy, it has to be an ongoing operation, whether it’s data security, privacy or observability,” he stated.

The post How Kyndryl is Charting Its Course in IT and AI appeared first on AIM.

DeepLearning.ai’s Andrew Ng recently launched a new course that focuses on quality and safety for LLM applications, in collaboration with WhyLabs (an AI Fund portfolio company). 

This one hour long course would be led by Bernease Herman, a senior data scientist at WhyLabs, where she will be focusing on the best practices to monitor LLM systems, alongside showcasing how you can mitigate hallucinations, data leakage, and jailbreaks among others. 

You can join the course here. 

New course with WhyLabs: Quality and Safety for LLM Applications

With the open source community booming, developers can prototype LLM applications quickly. In the introductory video Andrew Ng explained,“One huge barrier to the practical deployment has been quality and safety.” 

For a company that aims to launch a chatbot or a QA system, there is a good possibility that the LLM would hallucinate or mislead users. “It can say something inappropriate or can open up a new security loophole where a user can input a tricky prompt called the prompt injection that makes the LLM do something bad,” Andrew elaborated.

The course explains what can go wrong and the best practices to mitigate the problems including prompt injections, hallucinations, leakage of confidential PII like personal identifiable information, such as email or government ID numbers, and toxic or other inappropriate outputs. Bernease said, “This course is designed to help you discover and create metrics needed to monitor your LLM systems. For both safety and quality issues.”

Andrew Ng has consistently released courses on his DeepLearning.ai over the past year on Generative AI and its applications. These courses have helped learners increase their knowledge and expertise in AI and deep learning.

The post Andrew Ng Launches A New Course on LLM Quality and Security  appeared first on AIM.

Ever since the debut of ChatGPT powered by GPT-3.5 in December, there has been an emergence of several significant language models, including LLaMA, Llama 2, PaLM 2, GPT-4, Alpaca, Vicuna-13B, and more. Excitingly, this trend is poised to continue expanding. To fully harness the immense potential of generative AI and create customised models, these complimentary training programs will prove invaluable.

Generative AI for Everyone

Andrew Ng’s “Generative AI for Everyone,” is an in-depth course on generative AI. It covers its capabilities and boundaries, including practical exercises for everyday use, prompt engineering, and advanced AI techniques. The course focuses on real-world applications, showing generative AI’s common uses and allowing hands-on experience. It also discusses AI’s effect on business and society, preparing students to develop effective AI strategies and understand its real-world implications.

Introduction to LLMs

Google, which is currently building the next LLM Gemini, also provides a series of free courses on master generative AI. In this course, Google provides an overview of LLMs, and their definitions and explains potential applications. 

It also delves into the concept of prompt engineering, which can improve the performance of LLMs. Additionally, the module introduces various Google tools that can assist in the development of personalised generative AI applications.

LLMs Application through Production

This course by Databricks is designed for individuals with an intermediate-level proficiency in Python and a working understanding of machine learning and deep learning. It focuses on the practical application of LLMs through various frameworks. 

Participants will learn to build LLM-focused applications using popular libraries like Hugging Face and LangChain. The curriculum covers key concepts, including the distinctions between pre-training, fine-tuning, and prompt engineering. Industry experts, including Matei Zaharia (CTO Databricks and Associate Professor of CS at Stanford) and Harrison Chase (Co-Founder and CEO of LangChain), provide insightful lectures.

By the course’s conclusion, participants will have constructed an end-to-end LLM workflow, prepared for production deployment. This course is available for free audit, which means that you can access the course materials without paying any fee. However, if you want to access a managed compute environment for course labs, graded exercises, and a certificate, you need to pay a nominal fee.

Introduction to AI with Python

The introductory free online course “CS50’s Introduction to AI with Python” offered by Harvard School of Engineering and Applied Sciences covers fundamental concepts and algorithms in AI and ML using Python. It spans seven weeks, with a flexible time commitment of 10-30 hours per week and is self-paced.

Participants will explore various topics, including graph search algorithms, reinforcement learning, machine learning, and principles of artificial intelligence. The instructors, professors David J. Malan and Brian Yu, both from Harvard University, guide students through hands-on projects to reinforce theoretical knowledge. 

ChatGPT Prompt Engineering for Developers

Again created by DeepLearning.AI, this free course is in partnership with OpenAI and is taught by Isa Fulford (OpenAI) and Andrew Ng. It covers the essentials of prompt engineering for developers, from beginner to advanced levels. Participants will learn to effectively use LLMs, specifically using the OpenAI API, enabling them to swiftly develop innovative applications. 

The course explains LLM functionality, offers prompt engineering best practices, and demonstrates the practical application of LLM APIs in various tasks such as summarising, inferring, transforming text, and expanding content.

It also imparts two key principles for crafting effective prompts, systematic prompt engineering, and the creation of custom chatbots. The content is beginner-friendly, requiring only basic Python knowledge, while also catering to advanced machine learning engineers seeking cutting-edge insights into prompt engineering and LLM usage. 

Career Essentials in Generative AI

Microsoft and LinkedIn have come up with a free course titled “Career Essentials in Generative AI,” which serves as an introductory resource on generative AI and its practical applications. The course aims to furnish learners with fundamental skills essential for success in the generative AI domain. 

It covers key areas such as an overview of AI tools, an exploration of generative AI models and their functioning, differentiation between search engines and reasoning engines within the context of generative AI, using Microsoft Bing Chat for efficient work processes, a preview of Microsoft 365 Copilot, and a segment on the ethical considerations inherent in the generative AI creation and deployment process. 

The free course is structured to provide a comprehensive understanding of the subject matter, encompassing both technical aspects and ethical dimensions.

ChatGPT Prompt Book

The ChatGPT Prompt Book consists of over 300 unique writing prompts generated by the ChatGPT language model, designed for creative thinking and finding new ideas and perspectives. The prompts cover a diverse range of subjects and are adaptable to various writing styles and genres, making them useful for writers of all levels of experience, from beginners to professionals.

Generative AI Foundations on AWS

The Generative AI Foundations on AWS is an eight hour course on YouTube offering practical insights and hands-on guidance for pre-training, fine-tuning, and deploying foundational models on AWS. It emphasises breaking down theory, mathematics, and abstract concepts, providing hands-on exercises to build practical intuition. The course progressively explores complex generative AI techniques, enabling participants to understand, design, and apply their models effectively. 

Topics include the recap of foundation models, selecting the right model for specific use cases, pre-training new models, scaling laws for the model, dataset, and compute sizes, preparing training datasets on AWS at scale, fine-tuning models, and leveraging reinforcement learning with human feedback. 

Read more: Harish Sivaramakrishnan: The Creative Pulse of CRED

The post Top 8 Free Generative AI Courses appeared first on AIM.

Andrew Ng, the godfather of deep learning, has come up with “Vector Databases: from Embeddings to Applications”, a new free course on vector databases, their applications, and how they can be used to develop generative AI applications without training or fine-tuning an LLM. 

Vector databases play a crucial role in various fields, such as natural language processing, image recognition, recommender systems, and semantic search, and their importance has grown with the increasing adoption of LLMs. They provide LLMs with access to real-time proprietary data, enabling the development of Retrieval Augmented Generation (RAG) applications.

At their core, vector databases rely on embeddings to capture the meaning of data and determine the similarity between different pairs of vectors. The course aims to help learners gain the knowledge to make informed decisions about when to apply vector databases to their applications. 

The key topics that will be covered in this course include using vector databases and LLMs to gain deeper insights into data, building labs that demonstrate how to form embeddings using various search techniques to find similar embeddings and exploring algorithms for fast searches through vast datasets and building applications ranging from RAG to multilingual search.

Hallucinations in LLMs have been a persistent issue causing inaccurate and misleading outputs. Researchers have been exploring various solutions to address this problem, but the use of vector databases has shown promise in reducing the risk of hallucination.

Read more: Andrew OG of AI

The post Andrew Ng Launches New Free Course on Vector Database for LLMs appeared first on AIM.

With a plethora of generative AI courses that cater to learners seeking to make a difference in the AI job market, DeepLearning.AI founder Andrew Ng’s impact in AI education is significant. 

The consistent launch of new generative AI courses from him has been empowering individuals to pursue their desired AI careers. Notably, he founded an AI Fund of $175 million in 2018, underlining his commitment to the field.

DeepLearning.AI acknowledges Ng’s unparalleled influence in teaching the highest number of students globally, all outside of a traditional university setting. 

Here is a list of 5 new courses announced by Andrew Ng.

Building Computer Vision Applications

Andrew Ng will livestream a new course teaching students how to build custom computer vision models on November 6, 10.30 pm IST. The course covers crucial aspects, starting with the identification and scoping of vision applications. It explores technical feasibility, data quantity prerequisites, and selection of inputs and outputs for the vision model. 

Participants will explore the selection of appropriate vision project types or models, be it Object Detection, Semantic Segmentation, Image Classification, or other suitable models. Additionally, the course emphasises the application of Data-Centric AI, facilitating rapid iterative development through the systematic identification and resolution of data issues. 

Finally, the program covers the process of deploying a computer vision model, guiding learners from initial model development to live deployment, ensuring a comprehensive understanding of the entire pipeline.

Generative AI for Everyone

Starting on November 2, Andrew Ng’s new course, Generative AI for Everyone, provides an insightful exploration of generative AI, offering an understanding of its functionalities and limitations. Through hands-on exercises, participants learn practical applications for daily tasks and gain valuable insights into effective prompt engineering and advanced AI utilisation.

The curriculum delves into real-world applications, illustrating common use cases of generative AI. Participants have the opportunity to engage with generative AI tools, enabling the practical application of their knowledge. Moreover, the course offers a comprehensive understanding of AI’s influence on both business and societal landscapes.

By unpacking the impacts of generative AI on various sectors, the course equips learners with the tools to develop effective AI strategies and approaches, ensuring a well-rounded comprehension of AI’s application in real-world scenarios and its implications for both business and society.

Andrew Ng and LangChain

Andrew Ng launched a new generative AI course in collaboration with LangChain founder Harrison Chase. The course, ‘Functions, Tools, and Agents with LangChain‘, focuses on updating developers about advanced LLMs and LangChain usage for working with models.

Ng highlights the recent course developments, emphasising function calling like OpenAI’s LLMs and handling structured data efficiently. The updated training algorithms now comprehend and output data like JSON, providing students direct hands-on experience.

The enhancements lead to more predictable and reliable LLMs, proficient in tool usage for complex problem-solving. The course also introduces LangChain Expression Language (LCEL) to simplify composing chains and agents.

ChatGPT Prompt Engineering for Developers

This course is in partnership with OpenAI. The ChatGPT Prompt Engineering for Developers, is taught using large language models to swiftly create robust applications. The course by Isa Fulford (OpenAI) and Andrew Ng (DeepLearning.AI) explains LLM functionality, prompt engineering best practices, and practical API application. 

Students can explore tasks like summarising, inferring sentiment, text transformation, and automated content creation. Also principles for effective prompts, systematic prompt engineering, and building custom chatbots, demonstrated via various examples in our Jupyter notebook for direct hands-on experience are taught. 

Deep Learning Specialisation

In Ng’s Deep Learning Specialisation course, students can expect to learn clear, concise modules facilitating self-paced learning. It also teaches practical techniques to initiate AI projects and craft an industry portfolio along with foundational concepts, explained through easy-to-understand lectures and interactive assignments.

The content in this module remains up-to-date with the latest advancements in machine learning. Highly rated by over 120,000 learners with a score of 4.9 out of 5, it is one of the most-favoured data science programs on Coursera. This, however, is a longer course, paced at 10 hours a week for three months. 

Read More: 6 Brilliant New Free Courses by Andrew Ng on Generative AI

The post 5 New AI Courses Launched by Andrew Ng  appeared first on AIM.

Andrew Ng, announced a new generative AI course on LangChain in the series collaborating with Harrison Chase, founder of LangChain.  

Titled ‘Functions, Tools, and Agents with LangChain,’ developers can update themselves on the fast paced world of LLM and how to use LangChain to work with their models. 

In the earlier courses they explained how to use LangChain to chat and manipulate the data. “In the short time since we created those courses, there have been significant advancements in LLMs and the libraries to support the use as a developer tool,” Andrew Ng said. 

This course was created to update developers on function calling such as OpenAI’s LLM’s calling of other functions, which turns out to be very useful for handling structured data.

While the majority of work is done using formatted data, with function calls or API’s that want specific data in specific formats. The recent updates to training algorithms can now understand and output data like JSON, and in this course, students will get a chance to work with this directly. 

These updates make LLMs more predictable and reliable, as well as being better at understanding when to use tools. This makes it more feasible to build agents that can reason about how to use tools to solve multi step problems. 

Harrison Chase who explained the format of the course said, “In this course, we’ll start by explaining the recent advancements in LLM APIs. Next we’ll go over a new syntax that we at LangChain have introduced called LangChain Expression Language (LCEL) which makes it much easier to compose and customise chains and agents.” 

Students of the course will also explore the popular use cases, like structured data extraction, function calling and building up to a conversational agent. To register for the course you can follow this link. 

Simultaneously, Andrew Ng also announced a course to build Computer Vision models which will be livestreamed on the 6th of November. 

Read: Andrew Ng other courses

The post Andrew Ng Rolls Out New Generative AI Course on LangChain appeared first on AIM.

While AI has the potential to address the most complex global issues, it is crucial to use it responsibly and take into account the negative consequences of its application to mitigate harm. When companies jump onto the bandwagon of embracing emerging technologies without considering the broader social, economic, cultural, and political environments, they may jeopardise privacy and security while worsening existing inequalities. So, let’s delve into some of the top courses and certification programs to learn about ethics in AI.

Responsible AI Governance Badge Program

The EqualAI Badge Program, in partnership with the World Economic Forum, readies senior corporate leaders in AI-driven companies to establish a reputation for responsible and inclusive practices. Participants will gain expertise in creating and upholding responsible AI governance, become part of a supportive network of senior executives who share their values, and obtain certification through the EqualAI badge for mastering AI governance best practices.

AI Ethics

This Oxford University course on AI ethics covers fundamental concepts and broader philosophical considerations regarding AI’s ethical implications in our daily lives. The program starts by defining AI and distinguishing it from other machine learning methods, while also addressing the urgency of AI ethics. It then explores the ethics of AI creation, questioning its moral permissibility and the conditions for an AI to have moral significance. Next, the course delves into the philosophical aspects of designing ethical AI, including rule-based, bottom-up, and top-down approaches. It also examines the potential threats posed by AI to humanity and the feasibility of aligning our goals with AI’s objectives. Finally, the program scrutinizes the ethics surrounding specific AI applications, such as fully automated war drones, autonomous vehicles, robots, and AI-driven healthcare diagnostics.

Ethics of AI

The Ethics of AI is a free online course offered by the University of Helsinki, designed for those interested in the ethical dimensions of AI. The course seeks to educate individuals about AI ethics, exploring the boundaries of ethical AI development and encouraging ethical considerations in AI endeavours. Throughout the program, participants will work around the ethical dilemmas associated with responsible AI use and advancement, familiarise themselves with the ethical queries and principles relevant to modern AI and apply ethical theories and concepts practically to integrate them with AI applications.

Ethics of AI: Safeguarding Humanity

This MIT-led course equips you with the skills to handle ethical dilemmas in AI development and usage. It delves into AI’s ethical dimensions, spotlighting issues like machine bias and ethical hazards while prompting you to weigh your personal and organizational obligations. Over a three-day span, you’ll tackle the ethical facets of implementing AI at your workplace, gaining insights into harnessing AI for the greater good of humanity.

The Ethics of AI 

In this three-week online masterclass by the London School of Economics, participants will apply moral concepts like fairness, transparency, and inequality to real-world scenarios to effectively navigate ethical dilemmas, posed by AI. The course explores how AI is applied in various business contexts, such as hiring and employee oversight, and its implications for issues like discrimination and power imbalances. Participants will develop practical skills for immediate application, engaging in live sessions, ethical investigations in AI, and connecting with a global network of peers. By the end of the program, attendees will have a toolkit to navigate AI’s ethical dilemmas, critical thinking skills to debate key AI ethics issues, and an understanding of AI’s impact on inequality, resource distribution, and power dynamics in the workplace. This three-week online masterclass requires a commitment of 6-8 hours per week.

Certified Ethical Emerging Technologist

In this comprehensive program spanning five courses, our team of AI pioneers, ethical experts, and researchers will guide you in mastering the essential facets of ethics in data-driven technology. These modules cover fundamental ethical principles, industry-standard frameworks, how to identify and mitigate ethical risks, adept communication on ethical dilemmas, and the establishment of organizational governance crucial for fostering ethical, trusted, and inclusive data-driven innovations. Upon completing all five courses, you will have a know-how to bridge the gap between theory and practical application and how to apply ethical principles, frameworks, regulations, and standards within the realm of data-driven technologies, adept at recognizing and addressing ethical hazards in the entire lifecycle of such technologies, skilled in communicating effectively with a diverse range of stakeholders about ethical safeguards and risk mitigation strategies, and proficient in crafting, implementing, and assessing organisational policies and governance structures essential for maintaining ethical data-driven technologies.

Ethics in the Age of AI Specialisation

LearnQuest’s four-part course series covers a range of essential topics. Participants will first grasp the concept of predictive models and their practical applications in the business world. Next, they’ll explore the pervasive use of learning algorithms in daily life. The course will also delve into the potentially biased impact of algorithms on human behaviour and strategies to mitigate such bias. Lastly, participants will learn to pinpoint vulnerabilities within public data sets and assess violations of algorithmic privacy.

AI Ethics: Global Perspectives

This course aims to explain the societal consequences of technology and empower both individuals and organisations to engage in ethical and responsible utilisation of AI and data. Geared towards present and prospective data scientists, policymakers, and business executives, it introduces fresh monthly lectures centred on data and AI issues. Each module features a video presentation along with supplementary materials like videos, readings, and podcasts to enhance learning.

Read more: LLMs are an Ethical Nightmare 

The post Top 8 Courses & Certifications on AI Ethics  appeared first on AIM.

Ridding people of their fears of AI replacing jobs, Andrew Ng, the unassuming founder of DeepLearning.AI has been instrumental in AI literacy. Back in his iconic pale blue shirt, he added six new short courses that cover the current AI topics with a fresh set of partnerships, including Microsoft and Google. And the best part? These courses are offered free of charge (unless you want a certificate) and can typically be completed within one to two hours, making them easily accessible and time-efficient. Let’s delve into the details of these courses.

Join the Community >>

Large Language Models with Semantic Search

In partnership with Canadian startup Cohere, this course is tailored for beginners with a basic grasp of Python. It imparts valuable skills in augmenting keyword search using Cohere Rerank. It delves into the realm of advanced search techniques, instructing learners on seamlessly integrating LLMs into search systems. 

The course introduces the concept of dense retrieval, a powerful NLP tool that leverages embeddings to elevate the relevance of search results beyond conventional keyword-based methods. Participants will also gain insight into the intelligent reranking process, infusing LLMs’ intelligence into search systems for heightened efficiency and faster response times. 

Upon course completion, you will have a comprehensive idea about foundational principles of keyword search, transforming search systems using innovative reranking methods, embedding-based semantic understanding, and practical hands-on experiences. The course instructors, Jay Alammar and Luis Serrano from Cohere, ensure a comprehensive education in integrating language model-driven search functionalities into websites and projects to enhance user engagement and interactions.

Finetuning Large Language Models

This is a short course led by Sharon Zhou, the co-founder and chief executive of Lamini, who has also instructed in the GANs Specialisation and How Diffusion Models Work. Upon course completion, participants will understand when to employ finetuning techniques on LLMs, adeptly prepare data for this purpose, and successfully train and assess LLMs on their datasets.

Finetuning, a central focus of the course, enables individuals to customise LLMs using their own data, allowing for the adaptation of model weights and thus differentiation from alternative methods like prompt engineering and retrieval augmented generation. This process equips the model to acquire style, form, and incorporate new knowledge to enhance overall performance. Ideal candidates for this course should possess Python proficiency and a solid understanding of deep learning frameworks, particularly PyTorch. 

Building Generative AI Applications with Gradio

Ng has teamed up with Hugging Face to offer this new, concise course for beginners and will be instructed by Apolinário Passos, an ML Art Engineer at Hugging Face. Participants will delve into a range of tasks, such as image generation, image captioning, and text summarisation, using Gradio, an open-source Python library. 

Gradio empowers rapid development of user-friendly and adaptable UI components for machine learning models or APIs, enabling the creation of user-friendly applications even for those without coding expertise. Through Gradio, individuals can effortlessly construct intuitive graphical elements to interact with their models or APIs, ensuring accessibility and customization for users. By the course’s end, you will have acquired practical skills for developing interactive apps and demos, streamlining project validation and implementation.

Evaluating and Debugging Generative AI Models Using Weights and Biases

This course equips individuals with the skills to evaluate programs using LLMs and generative image models with platform-independent tools. Participants will discover how to instrument a training notebook, incorporating essential elements such as tracking, versioning, and logging. Moreover, they will gain expertise in monitoring and tracing LLMs’ performance over time in complex interactions. The course addresses the challenges of managing data sources, extensive data volumes, model development, parameter tuning, and experimentation in the realm of machine learning and AI projects. By introducing learners to Weights & Biases platform tools, this course simplifies experiment tracking, data running, and collaboration within a team. 

Key lessons cover Jupyter notebook instrumentation, hyperparameter configuration management, run metric logging, dataset and model versioning artifact collection, and experiment result logging. As a result, participants will develop a structured workflow that enhances productivity and expedites progress toward groundbreaking outcomes. It is suitable for those with Python and PyTorch familiarity and an interest in streamlining, versioning, and debugging their machine learning workflow. 

The instructor, Carey Phelps, is the founding product manager at Weights & Biases, bringing a wealth of expertise to guide learners through this transformative course.

Understanding and Applying Text Embeddings with Vertex AI

Participants can access this beginner-level course lasting an hour for free for a limited time. Instructed by Nikita Namjoshi, a developer advocate for generative AI at Google Cloud and Ng, the course focuses on leveraging text embeddings to capture the essence of sentences and paragraphs. You’ll learn how to utilize these numerical text representations for tasks like text clustering, classification, and identifying outliers. 

Furthermore, the course delves into constructing a question-answering system using Google Cloud’s Vertex AI. Participants will gain insights into word and sentence embeddings, measuring semantic similarity, text generation adjustments, and efficient semantic search using the ScaNN library. Upon completion, learners will have a solid grasp of text embeddings and their integration into Language Model applications. Basic Python knowledge is the only prerequisite for joining. 

How Business Thinkers Can Start Building AI Plugins With Semantic Kernel

In this latest course, you can delve into the world of Microsoft‘s open-source orchestrator, the Semantic Kernel. Offered free for a limited time, the course will teach learners how to enhance their business planning and analysis skills while harnessing the potential of AI tools. Throughout the course, students will advance their knowledge of LLMs, exploring techniques like using memories, connectors, chains, and more. Participants will be equipped to create sophisticated business applications using LLMs, effectively leverage LLM building blocks, and integrate the Semantic Kernel into their applications, streamlining AI service interactions without the need to learn multiple APIs. 

This course is suitable for anyone interested in learning Semantic Kernel, with basic Python skills; an understanding of APIs recommended but not mandatory. Instructor John Maeda, VP of design and atificial intelligence at Microsoft, guides learners through this invaluable learning journey.

Read more: Key Highlights of Google Cloud Next ‘23

The post 6 Brilliant New Free Courses by Andrew Ng on Generative AI appeared first on AIM.

AI education guru Andrew Ng has come up with a new course “Large Language Models with Semantic Search” in collaboration with Canadian AI startup Cohere. The free, short, beginner-friendly course will teach you to enhance keyword search using Cohere Rerank. Anyone with basic familiarity with Python can join the course. 

Course Highlights

Unlocking Advanced Search Techniques: This course equips learners with the essential techniques to seamlessly incorporate LLMs into keyword search systems.

Dense Retrieval Unveiled: Explore the concept of dense retrieval, a potent NLP tool. By harnessing embeddings, the course elevates the relevance of search results, surpassing traditional keyword-based approaches.

Intelligent Reranking: Gain insights into the reranking process, which injects the intelligence of LLMs into search systems. This strategic integration enhances search efficiency and expedites response times.

Upon completing the course, participants will learn how to use fundamental principles of keyword search, establishing the foundation for search systems that precede the availability of advanced language models. They will apply an innovative reranking technique to transform keyword search, prioritising responses based on query relevance and thereby enhancing the overall search encounter. Participants will also harness the power of embeddings to enable more profound search outcomes through semantic understanding. Practical expertise will be gained through hands-on experience, addressing real-world obstacles with substantial data and refining accuracy in handling diverse search results. Additionally, attendees will master the seamless integration of language model-driven search functionalities into websites or projects, thereby enhancing user engagement and interactions. 

Jay Alammar, Director and Engineering Fellow at Cohere and Luis Serrano, Lead of Developer Relations at Cohere are going to teach the course. 

Ng is a computer scientist-entrepreneur known for co-founding Coursera and founding DeepLearning.AI. He aims to democratise AI education and currently leads AI4ALL, a nonprofit promoting diversity in the AI workforce. To make this possible, he has partnered with various tech companies like OpenAI, AWS, and LangChain to come up with several AI courses.  Sign up for the course here.

Read more: Top 10 Free Specialised Courses by Andrew Ng

The post Andrew Ng & Cohere Unveil Free Course on LLMs with Semantic Search appeared first on AIM.

Every maths lover on the internet has once in their lifetime come across the name Professor Gilbert Strang. The YouTube star, even before it was a thing, devoted his entire life to the MIT maths department. 

In the early 2000s, MIT decided to create OpenCourseWare and instead of trying to sell courses, made them open to everyone. Then they recorded Strang’s ‘linear algebra course maths 18.06’. “In fact, I think India has more people who know that, of course, than any other country in the world,” Strang shared in an exclusive interview with AIM.

Read more: Inside The Mind of Gilbert Strang

Strang’s teaching methods remain irreplaceable even today as he “structures the class so that ideas seem to flow from the students into proofs”, said a former student, Jesse Michel. “Every class includes a cool maths trick or joke that keeps the class laughing,” he added.

The icon continues to stay relevant even after his retirement through his online courses and teaching methods. We have picked out his 6 most viewed and loved topics at MIT.

Linear Algebra 18.06

This lecture by Strang has achieved an impressive milestone, surpassing 10 million views on OpenCourseWare (OCW). This remarkable feat tells us why he is considered one of the most renowned mathematicians globally. At 88, Professor Strang delivered this lecture as his last, and received a standing ovation for his lifelong commitment to the field of mathematics.

Here’s the link to the course.

Mathematical Methods For Engineers (2)

Regarded as a graduate-level course, this lecture stands out as one of the finest discussions on numerical analysis available. Delivered during the Fall of 2000, the lecture delves profoundly into the difference methods for ordinary differential equations.

Here’s the link to the course. 

Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

A comprehensive understanding of linear algebra is key to mastering and developing machine learning algorithms, particularly in deep learning and neural networks. This course offers a thorough review of linear algebra, its practical applications in probability, statistics, and optimisation. Moreover, it presents an in-depth and clear explanation of the field of deep learning.

https://www.youtube.com/watch?v=t36jZG07MYc&t=1s 

Here’s the link to the course. 

Computational Science and Engineering (1)

Crafted in the Fall of 2008, this course is acclaimed for being a practical and valuable mathematics course. Students often express that it stands as one of the most useful maths courses they have ever encountered. The primary objectives of the course are to see the underlying patterns in numerous significant applications and to equip learners with methods to compute solutions.

Here’s the link to the course.

Highlights of Calculus 

In this course, Strang presented a concise series, providing a foundational introduction to the fundamental concepts of calculus, shedding light on its mechanics and significance. With an inclusive target audience, this series is resourceful for beginners as well as for educators pursuing self-learning.

Here’s the link to the course.

Wavelength, Filter Banks and Applications

The course written in Spring 2003 was co-instructed by Prof Kevin Amaratunga. The professors aim for the right balance of theory and “applications”. The course has no specific prerequisites, although a basic knowledge of Fourier transforms is recommended. The introduction begins with time-invariant filters and basic wavelets and further covers the analysis of filter banks and wavelets, design methods and so on. 

Here’s the link to the course. 

The post 6 Courses by Professor Gil Strang that the Internet Loves appeared first on AIM.

Generative AI holds the power to transform our customers’ operations, enhancing their efficiency, productivity, and innovation capabilities. As work methods continue to evolve, the demand for cloud expertise is increasing. Moreover, according to the World Economic Forum, AI skills represent the third-highest priority for companies’ training strategies, right alongside analytical and creative thinking. It is pretty much true that ‘AI might not replace you, but a person who uses AI could.’

To upskill individuals, AWS has introduced a diverse range of 7 generative AI courses, thoughtfully tailored to cater to both technical and non-technical audiences. Among these, 5 courses are specifically designed for developers and technical professionals, while the remaining 2 are geared towards those with a non-technical background.

While anyone can take these courses, however these are specifically designed for developers who want to utilize Amazon CodeWhisperer, engineers and data scientists who aim to employ generative AI by training and deploying foundation models (FMs), executives who want to gain insights into how generative AI can help address their business challenges and  AWS partners who wish to assist their customers in better understanding generative AI services and customer use cases.

Here is a list of  seven courses you can explore today to get started. 

For Technical Audience

Amazon CodeWhisperer – Getting Started : This self-paced digital course offers learners an introduction to Amazon CodeWhisperer, an AI coding companion that facilitates developers in accomplishing tasks more efficiently and quickly. 

It’s a free course designed to provide a comprehensive understanding of CodeWhisperer’s functionalities and how it can enhance the coding experience.In this course, you will learn how to install and start using CodeWhisperer in your supported integrated development environment (IDE) or code editor. 

AWS Jam Journey – Build Using Amazon CodeWhisperer :  It is a hands-on and interactive training tailored for DevOps professionals. Dive into practical challenges, building and exploring Amazon CodeWhisperer in a secure, sandboxed AWS environment. This unique learning opportunity comes bundled with an AWS Skill Builder subscription.

Generative AI Foundations on AWS :​ This free 8 hour, on-demand technical deep-dive course is specifically created for AI modeling experts. It offers conceptual fundamentals, practical insights, and hands-on instructions to pre-train, fine-tune, and deploy cutting-edge FMs on AWS and other platforms

Generative AI with Large Language Models : This course  is co-created by AWS, DeepLearning.AI, and machine learning pioneer Andrew Ng. This comprehensive three-week program equips data scientists and engineers with the skills to excel in selecting, training, fine-tuning, and deploying large language models (LLMs) for practical, real-world applications.

AWS PartnerCast – Building Generative AI on AWS: AWS PartnerCast offers an in-depth exploration of our generative AI services and capabilities on AWS, such as Amazon Bedrock, Amazon CodeWhisperer, and Amazon SageMaker. It demonstrates how organizations can effectively utilize these tools to assist their customers.

For Business and Nontechnical Audiences

AWS Partner: Generative AI on AWS Essentials (Business): This course tailored for AWS Partner customer-facing professionals. It covers the fundamentals of generative AI, explores essential customer use cases and personas, and illustrates how generative AI on AWS empowers customers to transform and revitalize their businesses.

Generative AI for Executives: It is a 13 minute fundamental generative AI course to help C-suite executives understand how generative AI can help address their business challenges and drive business growth.

The post Top 7 Generative AI Courses by AWS in 2024 appeared first on AIM.