This repository provides a comprehensive, open deep learning course developed by the Catalyst team, Tinkoff, and Deep Learning School. It targets students and practitioners seeking to learn modern deep learning techniques, covering topics from foundational concepts to advanced applications like NLP and recommender systems, with practical examples and homework assignments.

How It Works

The course is structured into weekly modules, each containing lecture notes and practical Jupyter notebooks. It leverages the Catalyst library, a PyTorch extension designed for simplifying deep learning experimentation, alongside PyTorch itself. This approach allows for rapid prototyping and efficient implementation of complex models and training pipelines.

Quick Start & Requirements

• Install: Create a Conda environment (conda create -n catalyst-dl python=3.7 anaconda), activate it (source activate catalyst-dl), and install dependencies (pip install -U catalyst==21.04.2 torch==1.8.0 albumentations==0.5.0).
• Prerequisites: Python 3.7, Conda, PyTorch 1.8.0, Catalyst 21.04.2, Albumentations 0.5.0.
• Running: Launch Jupyter Notebook (jupyter notebook --no-browser --ip 0.0.0.0 --port 8888).
• Resources: Official course syllabus and setup instructions are available within the repository.

Highlighted Details

• Covers a broad spectrum of deep learning topics, from basic backpropagation to advanced Transformer models and Reinforcement Learning for RecSys.
• Includes practical implementation details for CNNs, Object Detection, Metric Learning, Autoencoders, GANs, NLP, and Recommender Systems.
• Features homework assignments for hands-on practice.
• Some weeks are marked as Work In Progress (WIP), indicating ongoing development.

Maintenance & Community

The course is a collaborative effort with several contributors listed. Further community engagement details (e.g., Discord, Slack) are not specified in the README.

Licensing & Compatibility

The repository's license is not explicitly stated in the README. Compatibility for commercial use or closed-source linking would require clarification of the licensing terms.

Limitations & Caveats

The course is under active update, with some weeks marked as WIP. An older version of the full course is available on the v20.12 branch. The specified Python version is 3.7, which may require careful dependency management for newer environments.