Originally created by Meta, PyTorch has become an important tool for machine learning and people developing AI models.

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.

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.

Using PyTorch to streamline machine-learning projects A platform that lets surgeons browse videos of past operations has found a way to make its machine learning more effective.

The open-source PyTorch project is widely used for machine learning (ML) training. Its anticipated 2.0 version is now generally available.

IBM Research has contributed code to the open-source PyTorch machine learning project that could help to significantly accelerate training.

The PyTorch Foundation recently released PyTorch version 2.0, a 100% backward compatible update. The main API contribution of the release is a compile function for deep learning models, which ...

Positive and Unlabeled Learning (PUL) Using PyTorch Dr. James McCaffrey of Microsoft Research provides a code-driven tutorial on PUL problems, which often occur with security or medical data in cases ...

What is PyTorch? PyTorch is a deep learning framework designed to simplify AI model development. First released by Meta AI, it was built to improve the flexibility of deep learning research.