This repository outlines a 1-week intensive course designed to teach the fundamentals of deep learning and its application in modern AI, specifically focusing on building models from scratch and implementing influential research papers. It targets aspiring ML engineers and researchers seeking a practical, first-principles understanding of neural networks, from tensors to advanced architectures like Transformers and Stable Diffusion.

How It Works

The course progresses from foundational tensor operations and backpropagation to implementing classic architectures such as CNNs, RNNs, and then moves to cutting-edge models like Transformers and Stable Diffusion. It emphasizes a hands-on approach, encouraging learners to implement models based on research papers, fostering a deep understanding of how these complex systems are constructed and function.

Quick Start & Requirements

• Install/Run: No specific installation commands are provided, but the course implies the use of Python and common ML libraries.
• Prerequisites: Familiarity with Python programming is assumed. Access to computational resources (likely a GPU) will be necessary for implementing and running the models.
• Resources: Links to videos and code implementations are provided for each section.

Highlighted Details

• Covers foundational concepts like tensors, backpropagation, CNNs, and RNNs.
• Includes practical implementation guides for influential papers like LeNet, AlexNet, ResNet, DCGAN, LSTMs, GRUs, word2vec, Transformers, BERT, and Stable Diffusion.
• Structured into thematic sections, progressing from basic building blocks to complex vision-language models.

Maintenance & Community

No information on contributors, community channels, or roadmap is available in the README.

Licensing & Compatibility

The README does not specify a license.

Limitations & Caveats

The README presents this as a "rough outline" and a "1 week course," which may be overly ambitious for mastering all the covered topics from scratch. The depth of coverage for each model and the practical setup for running the code are not detailed.