This repository provides a comprehensive, self-paced course designed to guide individuals with Python proficiency from zero deep learning knowledge to implementing and training their own GPT models. It balances theoretical foundations with practical application, enabling users to tackle real-world AI problems.

How It Works

The course progresses sequentially through core deep learning concepts, starting with gradient descent and dense networks, then moving to recurrent networks and backpropagation. It utilizes PyTorch for practical implementation, covering text processing, transformer architectures, and distributed training. A key differentiator is the inclusion of advanced topics like implementing GPU kernels with OpenAI Triton and optimizing transformer efficiency for faster training.

Quick Start & Requirements

• Install: Clone the repository and run pip install -r requirements.txt.
• Prerequisites: Python 3.8 or higher.
• Resources: Local execution of notebooks is supported.

Highlighted Details

• Covers foundational math and NumPy for deep learning.
• Detailed explanation of backpropagation with a miniature PyTorch implementation.
• Includes chapters on text processing, transformers, and distributed training.
• Features advanced topics like GPU kernel implementation with OpenAI Triton and efficient transformers.

Maintenance & Community

The project is maintained by Vik Paruchuri and associated with Dataquest. Further details on community or roadmap are not explicitly provided in the README.

Licensing & Compatibility

Licensed under Creative Commons Attribution-NonCommercial 4.0 International License. This license permits use and adaptation for non-commercial courses, requiring attribution to Vik Paruchuri and Dataquest. Commercial use or linking with closed-source projects is restricted.

Limitations & Caveats

The course material is explicitly non-commercial. Some implementation notebooks and video lessons are marked as "coming soon," indicating potential incompleteness.