Google enhances TensorFlow with deep learning capabilities and parallelism techniques for developer choice in machine language tooling.

This PyTorch vs TensorFlow guide will provide more insight into both but each offers a powerful platform for designing and deploying machine learning models.

PyTorch recreates the graph on the fly at each iteration step. In contrast, TensorFlow by default creates a single data flow graph, optimizes the graph code for performance, and then trains the model.

TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet are the seven most popular frameworks for developing AI applications.

If you are wondering what Tensorflow is and why it is important in AI projects. TensorFlow is an open-source machine learning and AI platform ...

If you actually need a deep learning model, PyTorch and TensorFlow are both good choices ...

PyTorch introduced "Torchscript" and a JIT compiler, whereas TensorFlow announced that it would be moving to an "eager mode" of execution starting from version 2.0.

Is PyTorch better than TensorFlow for general use cases? This question was originally answered on Quora by Roman Trusov.

AI Platform Notebooks are configured with the core packages needed for TensorFlow and PyTorch environments. They also have the packages with the latest Nvidia driver for GPU-enabled instances.