This repository provides a comprehensive historical overview of deep learning, framed by seven key constraints that have historically limited progress. It's targeted at engineers and researchers seeking to understand the evolution of AI, from foundational concepts to state-of-the-art models like GPT-4o, offering insights into future directions.

How It Works

The project frames deep learning's advancement as a continuous effort to overcome seven fundamental constraints: data, parameters, optimization/regularization, architecture, compute, compute efficiency, and energy. Each constraint is explored through historical breakthroughs, demonstrating how overcoming these limitations has enabled increasingly capable AI systems. The repository includes curated papers, author's notes, explanations of key intuitions and mathematics, and PyTorch toy implementations.

Quick Start & Requirements

• Install: No explicit installation instructions are provided for the core content, which is primarily text-based. PyTorch is used for toy implementations.
• Prerequisites: Python 3.x, PyTorch. Access to GPUs (e.g., A100s) is mentioned for training larger models.
• Resources: Links to PDF papers and .ipynb files for implementations are provided within the repository structure.

Highlighted Details

• Constraint-Based Framework: A novel approach to understanding AI progress through the lens of seven limiting factors.
• Historical Deep Dive: Covers key milestones from early feed-forward networks to modern architectures like Transformers and Diffusion models.
• Curated Resources: Provides direct access to seminal research papers with accompanying explanations and code.
• Future Outlook: Extrapolates current trends and constraints to predict future directions in AI development.

Maintenance & Community

The project appears to be a personal deep-dive by adam-maj, with acknowledgments to Pavan Jayasinha and Anand Majmudar for feedback. No specific community channels or active maintenance indicators are present.

Licensing & Compatibility

The repository does not explicitly state a license. The content is educational and research-oriented.

Limitations & Caveats

The project is a curated collection of information and implementations rather than a runnable library. The toy implementations are for demonstration and may not represent production-ready code. The historical narrative is presented from the author's perspective.