BERT-related papers. Contribute to tomohideshibata/BERT-related-papers development by creating an account on GitHub.

Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they...

Learn how to build build a recurrent neural network to do French to English translation using Google's open-source machine learning library, TensorFlow. ​​

An open source machine learning framework that accelerates the path from research prototyping to production deployment.

We are open-sourcing Detectron2, the second-generation of our widely used object-recognition platform. Detectron2 has been rewritten from the ground up in PyTorch to enable faster model iteration and deployment.

Posted by Ankur Bapna, Software Engineer and Orhan Firat, Research Scientist, Google Research “... perhaps the way [of translation] is to descend...

Facebook AI is open-sourcing CrypTen, a research-focused framework to explore encrypted machine learning techniques in the PyTorch environment.

A collection of infrastructure and tools for research in neural network interpretability. - tensorflow/lucid

We’re open-sourcing a new platform for experimenting with reinforcement learning to proactively adapt to changing traffic patterns.

A guest post by Sandeep Mistry & Dominic Pajak of the Arduino team

We’ve trained a pair of neural networks to solve the Rubik’s Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic…

Deep recommender models using PyTorch. Contribute to maciejkula/spotlight development by creating an account on GitHub.

Facebook AI is open-sourcing ReAgent, the most comprehensive and modular open source platform for creating AI-based reasoning systems, and the first to include policy evaluation to incorporate offline feedback.

In this tutorial you are going to learn about the Naive Bayes algorithm including how it works and how to implement it from scratch in Python (without libraries). We can use probability to make predictions in machine learning. Perhaps the most widely used…

Our Transformers library implements several state-of-the-art transformer architectures used for NLP tasks like text classification…