2020

In this post, I would like to share with you our development progress for the last month. Let’s check what features we have added to the framework in such a short time.

For real breakthroughs in deep learning, we need a strong foundation. In this blog post, I would like to introduce Catalyst framework, developed with focus on reproducibility, fast experimentation and code/idea reusing. We’ll also provide a tutorial on MNIST classification problem as an example.

Checklist and questions to make ML right.

Accelerated Python code formatter

In this episode, Sanyam Bhutani interviews a researcher, practitioner and open source contributor: Sergey Kolesnikov, creator of catalyst, which is a deep learning and reinforcement learning framework based on Pytorch.

We present Catalyst.RL, an open-source PyTorch framework for reproducible and sample efficient reinforcement learning (RL) research. Main features of Catalyst.RL include large-scale asynchronous distributed training, efficient implementations of …

Brief introduction to Reinforcement Learning and Recommender Systems.

Every year the topic of reinforcement learning (RL) is getting hotter and more hype. And every year, DeepMind and OpenAI add fuel to the fire with a new superhuman performance bot. Is there something really worthwhile behind this? And what are the latest trends in the entire RL variety? Let's find out!