21 stories
Deep learning is not a single library command that turns data into intelligence. A useful model depends on the problem definition, data quality, mathematical assumptions, architecture, training loop, evaluation, deployment and monitoring.
If you’re learning deep learning, you’ve likely searched for a clear tensorflow introduction before jumping into code. TensorFlow remains one of the most in-demand frameworks in data science hiring, used across computer vision, NLP, and recommendation systems at companies like Google, Airbnb, and…
A forger, a detective, and a game that never ends: the full, friendly guide to how two neural networks compete to produce data that never existed before.
Bad tool calls, malformed output, and token spend you cannot account for. Agent tracing shows all of it. Serge from Sentry instruments three agents live: a chatbot in an ecommerce store, a custom Slack agent, and a GitHub Action that reviews PRs.
You can finish three deep learning courses, build a CNN, and still find yourself stuck when the same model stops working on your own dataset. Maybe changing a few parameters does nothing, the validation loss starts behaving strangely, or the model simply refuses to learn.
TensorFlow 2.20 deprecates tf.lite for LiteRT, enhances input pipeline warm-up speed, and makes installation of tensorflow-io-gcs-filesystem optional.
TensorFlow 2.19 has been released, including changes to the C++ API in LiteRT, the discontinuation of releasing libtensorflow packages, and more.
TensorFlow 2.18 is available now, with NumPy 2.0 support, LiteRT repository, CUDA updates, Hermetic CUDA for improved build reproducibility, and more.
TensorFlow 2.17 features CUDA updates for improved performance on Ada-Generation GPUs and upcoming support for Numpy 2.0. in TensorFlow 2.18.
We are thrilled to introduce {keras3}, the next version of the Keras R package. {keras3} is a ground-up rebuild of {keras}, maintaining the beloved features of the original while refining and simplifying the API based on valuable insights gathered over the past few years.
TensorFlow 2.16 has been released. Highlights include Clang as default compiler for building TensorFlow CPU wheels on Windows, and more.
Posted by Dustin Zelle, Software Engineer, Google Research, and Arno Eigenwillig, Software Engineer, CoreML Objects and their relationships are ubi...
Announcing the release of TensorFlow GNN 1.0, a production-tested library for building GNNs at Google scale, supporting both modeling and training.
We are releasing a hot-fix for an installation issue affecting the TensorFlow installation process.
TensorFlow 2.15 has been released! Highlights include a much simpler installation method for NVIDIA CUDA libraries for Linux and more.
Spotify shares how they use TensorFlow and Reinforcement Learning to train models offline, translating results to large scale, online performance.
TensorFlow 2.13 has been released! We're highlighting the new Keras V3 format as default for .keras extension files and much more!
Implementation and walk-through of LLaMA, a Large Language Model, in R, with TensorFlow and Keras.
Showing 18 of 21 stories
Load more storiesGet the top AI news and tools straight to your inbox every week.
Weekly · AI news
Join 15,000+ AI enthusiasts
