The second PyTorch release of the year has landed. PyTorch 1.5 brings some of the deep learning library’s previously experimental features into stable mode, including the C++ frontend API. Let’s take a closer look and see what that means—and what else has been updated in this release.
PyTorch3D is the latest deep learning tool by Facebook AI. The open source tool is designed to integrate with PyTorch to make 3D deep learning easier. Along with it, the codebase of the 3D shape prediction method Mesh R-CNN, which was built with the help of PyTorch3D, has been released as well.
The AI research organization OpenAI has declared to standardize which deep learning framework to use in its projects, and the winner is PyTorch. Let’s take a closer look and see not only why OpenAI selected PyTorch, but also what benefits the standardization itself should offer.
The deep learning platform PyTorch has received an upgrade. Version 1.4 comes with breaking changes, new features, bug fixes and deprecations. Java bindings are available as one of several experimental features, and you can now use the latest versions of PyTorch’s domain libraries.
The newest update for PyTorch-NLP is here. The 0.5.0 update adds support for Python 3.5, PyTorch 1.2, rewrites the README to help new users build an NLP pipeline, and adds some new features. See how PyTorch-NLP helps with natural language processing and how PyTorch compares to similar machine learning frameworks such as TensorFlow.
The success of deep neural networks in diverse areas as image recognition and natural language processing has been outstanding in recent years. However, classical machine learning and deep learning algorithms can only propose the most probable solutions and are not able to adequately model uncertainty.
