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.

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

Both PyTorch and TensorFlow support deep learning and transfer learning. Transfer learning, which is sometimes called custom machine learning, starts with a pre-trained neural network model and ...

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

Alibaba and Huawei aren't the only ones working to end China's reliance on Western silicon. Last month, EE Times China reported that Tencent-backed startup Enflame was developing a new AI chip called ...

First is PyTorch, with its tremendous following and mindshare. If you look at the metrics alone it might be easy to miss, but PyTorch is quite possibly the most used and talked about deep learning ...

TensorFlow uses a dataflow graph to represent computations. It shares this space with another open-source machine-learning framework called PyTorch.

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.

TensorFlow Lite for mobile on-device AI has “grown beyond its TensorFlow roots to support models authored in PyTorch, JAX, and Keras.” ...