TensorFlow also lost steam in the research community, which started preferring the flexibility PyTorch offered a few years ago, resulting in a decline in the use of TensorFlow in research papers.

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

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

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

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.

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

There are tools to convert Tensorflow, PyTorch, XGBoost, and LibSVM models into formats that CoreML and ML Kit understand. But other solutions try to provide a platform-agnostic layer for training ...

OneDNN is an open-source cross-platform library intended for devs of deep learning applications and frameworks such as TensorFlow.