This repository contains learning notes for the 2021 Deep Learning course by Professor Li Hongyi. It serves as a comprehensive resource for students and practitioners seeking to understand fundamental and advanced deep learning concepts, including regression, classification, CNNs, Transformers, GANs, BERT, autoencoders, adversarial attacks, explainable AI, and reinforcement learning.

How It Works

The repository is structured as a collection of detailed notes, likely derived from lecture transcripts or summaries. Each note focuses on a specific topic within the deep learning curriculum, providing explanations and insights into the underlying principles and methodologies. The organization follows a logical progression, starting with foundational concepts and moving towards more complex architectures and applications.

Quick Start & Requirements

This repository is primarily a collection of notes and does not require installation or execution. The content is accessible via HTML links provided in the README.

Highlighted Details

• Comprehensive coverage of core deep learning topics.
• Detailed notes on Transformer architectures and Self-Attention mechanisms.
• In-depth exploration of Generative Adversarial Networks (GANs).
• Extensive sections on BERT, autoencoders, and adversarial attacks.

Maintenance & Community

This repository appears to be a static collection of notes, with no active development or community interaction indicated.

Licensing & Compatibility

The repository does not specify a license.

Limitations & Caveats

As this is a collection of learning notes, it does not contain executable code or models. The content is static and may not reflect the latest advancements in the field beyond the 2021 curriculum.