No longer the upstart nipping at TensorFlow’s heels, PyTorch is a major force in the deep learning world today, perhaps primarily for research, but also in production applications more and more.

If this is what matters most for you, then your choice is probably TensorFlow. A network written in PyTorch is a Dynamic Computational Graph (DCG). It allows you to do any crazy thing you want to do.

TensorFlow is your ally for scalability and production. PyTorch is your friend for research flexibility and ease of use. The choice depends on your project needs, expertise, and long-term goals.

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.

There is a C++ API, but there isn’t half the support for other languages that TensorFlow offers. It’s quite conceivable that PyTorch will overtake TensorFlow within Python.

Developers can submit ML training jobs created in TensorFlow, Keras, PyTorch, Scikit-learn, and XGBoost. Google now offers in-built algorithms based on linear classifier, wide and deep and XGBoost ...

This article will discuss the seven popular tools and frameworks used for developing AI applications: TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet.