PyTorch is a Python-based tensor computing library with high-level support for neural network architectures. It also supports offloading computation to GPUs.

The Anaconda distribution of Python contains a base Python engine plus over 500 add-in packages that have been tested to be compatible with one another. After you have a Python distribution installed, ...

It's important to document the versions of Python and PyTorch being used because both systems are under continuous development. Dealing with versioning incompatibilities is a significant headache when ...

But why should you choose to use PyTorch instead of other frameworks like MXNet, Chainer, or TensorFlow? Let’s look into five reasons that add up to a strong case for PyTorch.

AI has become one of the great, meaningless buzzwords of our time. In this video, the Chief Data Scientist of Dun and Bradstreet explains AI in clear business terms. The Python-based PyTorch 1.0 ...

This is similar to PyTorch's eager mode in both advantages and shortcomings. It helps with debugging, but then models cannot be exported outside of Python, be optimized, run on mobile, etc.

As the popularity of the Python programming language persists, a user survey of search topics identifies a growing focus on AI and machine learning tasks and, with them, greater adoption of related ...

Facebook Inc. today revealed that it’s going all-in on PyTorch as its default artificial intelligence framework. The company said that by migrating all of its AI systems to PyTorch, it will be ...