This repository provides minimal, annotated, from-scratch PyTorch implementations of modern deep learning techniques, targeting aspiring AI researchers and practitioners. It aims to demystify complex concepts by offering self-contained code with detailed explanations, enabling users to build a strong practical understanding of frontier AI models and algorithms.

How It Works

The project breaks down advanced deep learning into digestible components, implementing key architectures (Transformers, ViT, DiT, RNNs, ResNets), attention variants (GQA, Linear, Sparse), training techniques (optimized dataloading, BPE), inference optimizations (KV caching, speculative decoding), and Reinforcement Learning algorithms (DQN, REINFORCE, PPO). Each implementation is designed for clarity and educational value, with extensive inline comments explaining subtle implementation details often omitted in research papers or production code.

Quick Start & Requirements

• Install dependencies: pip install torch numpy torchvision wandb tqdm transformers datasets diffusers matplotlib pillow jupyter gym
• Requires a GPU for practical execution speed.
• Code can be run directly via Python scripts (e.g., python architectures/train_dit.py).
• Jupyter notebooks are available for step-through learning.

Highlighted Details

• Comprehensive coverage of Transformer architectures, attention mechanisms, and inference optimizations.
• Includes implementations for various Reinforcement Learning paradigms, from classical to actor-critic.
• Features diffusion model implementations and GANs for image generation.
• Focuses on self-contained, readable PyTorch code with detailed explanations.

Maintenance & Community

The author is actively implementing new techniques and welcomes contributions and bug fixes. Direct contact is available via email (tanishq@stanford.edu) for feedback and requests.

Licensing & Compatibility

The repository does not explicitly state a license in the provided README. Users should verify licensing for commercial or closed-source use.

Limitations & Caveats

While designed to run on a single GPU, many implementations will be prohibitively slow without one. Several advanced topics like LSTM, MoE, RLHF, DPO, and distributed MLSys techniques are listed as "coming soon" or "in progress," indicating the project is still under active development.