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.

They put all their eggs in the PyTorch basket. Second is TensorFlow. It is used for many of the same use cases as PyTorch. It is easy to find it attached to use cases for speech recognition, object ...

I’ve previously reviewed PyTorch 1.0.1 and compared TensorFlow and PyTorch. I suggest reading the review for an in-depth discussion of PyTorch’s architecture and how the library works.

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 ...

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.