Stay updated on the latest TensorFlow developments, releases, and innovations. Get insights into new features, performance improvements, and best practices for this popular open-source machine learning framework. Discover how TensorFlow is advancing AI across various industries, from computer vision to natural language processing. Learn about community contributions, research breakthroughs, and practical applications of TensorFlow in real-world scenarios.
One notable improvement is the added support for Python 3.12, showing TensorFlow’s effort to stay current with the latest language releases.
It simplifies the creation of models that understand complex relationships within data, from social networks to logistics.
“What we’ve seen with generative AI is the ability for it to seemingly reason about test scenarios that that could be interesting but may have been overlooked,” said Rangarajan Vasudevan,
ExecuTorch is a practical choice for broad model compatibility or Android device support
Java has flexible capabilities, vast libraries, and with endorsements from major tech companies the language is gaining traction in Machine learning.
Python still remains a dominant force in AI development, with more than 275,495 companies using it.
Google was leading with TensorFlow, but Meta’s PyTorch won hearts with the ease of use, and things have stayed that way
Major improvements have been made to Keras as well.
From data visualisation to deep learning libraries, Python is the most valuable language for machine learning.
The library aims to address key engineering challenges in scientific computing through better management and processing of large datasets.
TensorFlow’s team is now introducing Decision Forests 1.0 with the latest release
Preprocessing prevents from overfitting
XLA is a compiler used to accelerate training time of Tensorflow models and reduce memory consumption.
This article mainly focuses on the Tensor2Tensor library and to understand the dynamic abilities to handle and process complex models.
Tensorflow model remediation is a framework used to obtain fairness free and bias free models. It aims to produce robust models that is not affected by sensitive attributes of data.
The weight clustering API is one of the use cases of the Tensorflow model optimization library and it aims to optimize the models developed so that they can be easily
This article has explained the importance of Tensorflow probability and its working principle. It has also explained the working principle of Tensorflow probability and its importance in the context of
Tensorflow lattice modelling aims to obtain a more reliable and generic model which perfroms phenomanally when taken up for testing for similar kind of data it is trained upon.
Do you want to know how kernel regularizers adds penalty terms to the network weights and optimize performance. Here is the answer.
How to develop deep learning models in edge devices? Here is the answer
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
On-device machine learning uses a simplified version of cloud-based machine learning.
Sonnet creates high-level networks that are easier to train and test with multiple applications.
It works by using a model to embed the search query into a high-dimensional vector representing the semantic meaning of the query.
The main highlights of this release are performance enhancement with oneDNN and the release of a new API for model distribution, called DTensor
Tensorflow has launched a live demo for enthusiasts to try and convert their photographs into 3D versions.
MoViNets are a family of CNNs that efficiently process video streams and accurate output predictions with a fraction of the latency of CNN video classifiers.
The TensorFlow library is an implementation from TensorFlow that helps us in building learning-to-rank (LTR) models. The learning to rank(LTR) models are models that help us in constructing the ranking
This article we will walk you through and compare the code usability and ease to use of TensorFlow and PyTorch on the most widely used MNIST dataset to classify handwritten
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