Machine learning in Python is powerful through the facilities of TensorFlow and Scikit-learn. While TensorFlow has broader applications in advanced deep learning-based complex models with large ...

Hi @lxzheng , As per migration documentation here saving a keras3 model into tf saved_model needs to use tf.saved_model.save API but not model.save. This is the only limitation AFAIK.

Many developers who use Python for machine learning are now switching to PyTorch. Find out why and what the future could hold for TensorFlow.

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on its own GPUs (graphics processing units).

Accelerate Linear Algebra (XLA) is the compiler designed to accelerate Tensorflow models to speed up the training process and reduce the overall memory consumption. Tensorflow operations are split ...

The purpose of MLGO is to replace this heuristic with a machine learning model. The compiler consults a neural network throughout the call graph traversal to determine whether to inline a specific ...

TensorFlow Recommenders (TFRS) is an open-source TensorFlow package that simplifies the building, evaluation, and deployment of advanced recommender models.