Posted by the TensorFlow teamTensorFlow 2.20 has been released! For ongoing updates related to the multi-backend Keras, please note that all news and releases, starting with Keras 3.0, are now published directly on keras.io. You can find a complete list of all changes in the full release notes on GitHub.| The TensorFlow Blog
Posted by the TensorFlow teamTensorFlow 2.19 has been released! Highlights of this release include changes to the C++ API in LiteRT, bfloat16 support for tflite casting, discontinue of releasing libtensorflow packages. Learn more by reading the full release notes.| The TensorFlow Blog
Posted by Colby Banbury, Emil Njor, Andrea Mattia Garavagno, Vijay Janapa Reddi – Harvard UniversityTinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge devices. However, the growth of this field has been stifled by a lack of tailored large and high-quality datasets. That's where Wake Vision comes in—a new dataset designed to accelerate research and development in TinyML.| The TensorFlow Blog
Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University)Everyone wants to do the modeling work, but no one wants to do the engineering.| The TensorFlow Blog
Posted by the TensorFlow teamTensorFlow 2.18 has been released! Highlights of this release (and 2.17) include NumPy 2.0, LiteRT repository, CUDA Update, Hermetic CUDA and more. For the full release notes, please click here.| The TensorFlow Blog
Posted by the TensorFlow teamTensorFlow 2.17 has been released! Highlights of this release (and 2.16) include CUDA update, upcoming Numpy 2.0, and more. For the full release notes, please click here.| The TensorFlow Blog
Posted by Alan Kelly, Software EngineerWe are excited to announce that XNNPack’s Fully Connected and Convolution 2D operators now support dynamic range quantization. XNNPack is TensorFlow Lite’s CPU backend and CPUs deliver the widest reach for ML inference and remain the default target for TensorFlow Lite. Consequently, improving CPU inference performance is a top priority. We quadrupled inference performance in TensorFlow Lite’s XNNPack backend compared to the single precision baselin...| The TensorFlow Blog
Posted by the TensorFlow teamTensorFlow 2.16 has been released! Highlights of this release (and 2.15) include Clang as default compiler for building TensorFlow CPU wheels on Windows, Keras 3 as default version, support for Python 3.12, and much more! For the full release note, please click here.| The TensorFlow Blog
Posted by the TensorFlow teamWe are releasing a hot-fix for an installation issue affecting the TensorFlow installation process. The TensorFlow 2.15.0 Python package was released such that it requested tensorrt-related packages that cannot be found unless the user installs them beforehand or provides additional installation flags. This dependency affected anyone installing TensorFlow 2.15 alongside NVIDIA CUDA dependencies via pip install tensorflow[and-cuda]. Depending on the installation me...| The TensorFlow Blog
Posted by Marat Dukhan and Frank Barchard, Software Engineers| The TensorFlow Blog
Posted by the TensorFlow teamTensorFlow 2.15 has been released! Highlights of this release (and 2.14) include a much simpler installation method for NVIDIA CUDA libraries for Linux, oneDNN CPU performance optimizations for Windows x64 and x86, full availability of tf.function types, an upgrade to Clang 17.0.1, and much more! For the full release note, please check here.| The TensorFlow Blog
Posted by Sharbani Roy – Senior Director, Product Management, Google | The TensorFlow Blog
Posted by Wei Wei, Developer Advocate| The TensorFlow Blog
Posted by Ashley Oldacre| The TensorFlow Blog
Posted by Google: Mathieu Guillame-Bert, Richard Stotz, Robert Crowe, Luiz GUStavo Martins (Gus), Ashley Oldacre, Kris Tonthat, Glenn Cameron, and Tryolabs: Ian Spektor, Braulio Rios, Guillermo Etchebarne, Diego Marvid, Lucas Micol, Gonzalo Marín, Alan Descoins, Agustina Pizarro, Lucía Aguilar, Martin Alcala RubiTemporal data is omnipresent in applied machine learning applications. Data often changes over time or is only available or valuable at a certain point in time. For example, market ...| The TensorFlow Blog
Posted by Ruijiao Sun, Google Intern - DTensor teamFast Fourier Transform is an important method of signal processing, which is commonly used in a number of ways, including speeding up convolutions, extracting features, and regularizing models. Distributed Fast Fourier Transform (Distributed FFT) offers a way to compute Fourier Transforms in models that work with image-like datasets that are too large to fit into the memory of a single accelerator device. In a previous Google Research Paper,...| The TensorFlow Blog
Posted by Paul Ruiz, Developer Relations EngineerWe're excited to announce that the TensorFlow Lite plugin for Flutter has been officially migrated to the TensorFlow GitHub account and released!| The TensorFlow Blog
Posted by Alan Kelly, Software EngineerOne of our previous articles, Optimizing TensorFlow Lite Runtime Memory, discusses how TFLite’s memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on even smaller edge devices. In today’s article, I will describe the performance optimization of the memory arena initialization so that our users get the benefit of low memory usage with little additional overhead.| The TensorFlow Blog
Posted by the TensorFlow and Keras TeamsTensorFlow 2.13 and Keras 2.13 have been released! Highlights of this release include publishing Apple Silicon wheels, the new Keras V3 format being default for .keras extension files and many more!| The TensorFlow Blog
Posted by Angelica Willis and Akib Uddin, Health AI Team, Google ResearchHow researchers at Google are working to expand global access to maternal healthcare with the help of AI| The TensorFlow Blog
Posted by Wei Wei, Developer AdvocateLarge language models (LLMs) are taking the world by storm, thanks to their powerful ability to generate text, translate languages, and answer questions in a coherent and informative way. At Google I/O 2023, we released the PaLM API as ‘public preview’ so that many developers can start building apps with it. While PaLM API already has excellent documentation on its extensive usage and best practices, in this blog we are going to take a more focused app...| The TensorFlow Blog
Posted by Terence Parr, GoogleDecision trees are the fundamental building block of Gradient Boosted Trees and Random Forests, the two most popular machine learning models for tabular data. To learn how decision trees work and how to interpret your models, visualization is essential.| The TensorFlow Blog
Attend our first Developer Summit on Recommendation System on Jun 9, 2023.| blog.tensorflow.org
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.| blog.tensorflow.org
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.| blog.tensorflow.org
Fine tune BERT for Sentiment analysis using TensorFlow Hub| blog.tensorflow.org
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.| blog.tensorflow.org
Arduino is on a mission to make machine learning easy enough for anyone to use. The other week we announced the availability of TensorFlow Lite Micro in the Arduino Library Manager. With this, some cool ready-made ML examples such as speech recognition, simple machine vision and even an end-to-end gesture recognition training tutorial. For a comprehensive background we recommend you take a look at that article. In this article we are going to walk through an even simpler end-to-end tutorial u...| blog.tensorflow.org
Announcing the release of TensorFlow GNN 1.0, a production-tested library for building GNNs at Google scale, supporting both modeling and training.| blog.tensorflow.org
Spotify shares how they use TensorFlow and Reinforcement Learning to train models offline, translating results to large scale, online performance.| blog.tensorflow.org