The Keras 3.1.0 update introduces enhanced model optimisation and flexibility, streamlining AI development for developers across various platforms.
ExecuTorch is a practical choice for broad model compatibility or Android device support
From data visualisation to deep learning libraries, Python is the most valuable language for machine learning.
Preprocessing prevents from overfitting
The FNet Encoder is based on the Fourier transform
Hyperband is a framework for tuning hyperparameters
A detailed implementation of usage of Comet platform for deploying and monitoring a model.
This article briefs about the various methods to serialize and deserialize Scikit Learn and Tensorflow models for production
The primary author and maintainer of Keras is Google engineer François Chollet.
TFRS is built on top of Tensorflow 2 and Keras.
Torchbearer is python based library which is basically a model fitting library for PyTorch models and offers a high-level metric and callback API that can be used in a variety
In this post, we’ll look at TensorLayer, a Python-based machine learning tool.
From this post you will come to know how particular predictions are being made and how models focus on various aspects of parameters it has learned.
A comparative analysis of open-source deep learning optimization libraries DeepSpeed and Horovod for advancing large-scale model training.
Python is an interpreted, interactive, portable and object-oriented programming language. This open-sourced general-purpose language runs on many Unix variants, including Linux and macOS, and Windows. Python has applications in hacking,
Text Generation is a task in Natural Language Processing in which text is generated with some constraints such as initial characters words
Sentiment Analysis is a text classification application in which a given text is classified into either a positive class or a negative class
Image Generation is one of the most curious applications in Computer Vision. Variational Autoencoders and GANs are the preferred base models
Semantic segmentation in computer vision is the supervised process of pixel-level image classification into two or more Object classes
Transfer Learning is the approach of making use of an already trained deep learning model along with its weights for a related task
Computer Vision attempts what a human brain does with the aid of eyes. It is a branch of Deep Learning that deals with images and videos.
Deep Learning is a subset of Machine learning. It was developed to have an architecture and functionality similar to that of a human brain.
Ever since Google has publicised Tensorflow, its application in Deep Learning has been increasing tremendously. It is used even more in research and production for authoring ML algorithms. Though it
KGCNN offers a straightforward and flexible integration of graph operations into the Tensorflow-Keras framework using RaggedTensors.
Einops, an abbreviation of Einstein-Inspired Notation for operations is an open-source python framework for writing deep learning code in a new and better way. Einops provides us with new notation
We have already covered the PyTorch loss functions implementations in our previous article, now we are heading forward to the other libraries that have been used more widely than PyTorch,
Internet Movie Database (IMDb) is an online information base committed to a wide range of data about a wide scope of film substance, for example, movies, TV and web-based streaming
HMDB-51 is an activity video information dataset with 51 activity classifications, which altogether contain around 7,000 physically clarified cuts separated from an assortment of sources going from digitized motion pictures
ActivityNet is an enormous dataset that covers exercises that are generally pertinent to how people invest their energy in their everyday living. It was developed in 2015 by the researchers:
In this article, I’ll discuss the deep learning frameworks available for different programming language interfaces.
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