This repository is a curated collection of seminal research papers in machine learning, presented as a textbook for understanding the historical context and foundational concepts of the field. It targets ML practitioners, researchers, and students seeking to deepen their theoretical understanding by referencing original sources. The benefit is a structured, comprehensive overview of ML's evolution, from classic algorithms to modern deep learning architectures.

How It Works

The collection is organized thematically, covering areas such as classical ML algorithms (Lasso, SVM, Decision Trees), deep learning components (CNNs, RNNs, Transformers), reinforcement learning, generative models (GANs, Diffusion Models), and theoretical underpinnings (VC dimension, Information Theory). Each entry links to the original paper, providing context on the problem it solved, its novelty at the time, and competing approaches.

Quick Start & Requirements

No installation or execution is required. This is a reference collection of papers. Links to papers and related resources are provided within the README.

Highlighted Details

• Comprehensive coverage spanning from early statistical methods to state-of-the-art deep learning.
• Includes foundational papers for key algorithms like SVM, AdaBoost, XGBoost, and Transformers.
• Features significant works in computer vision (AlexNet, GANs, NeRF, ViT) and NLP (BERT, GPT).
• Covers theoretical aspects like generalization, regularization, and information theory.

Maintenance & Community

This is a static collection of links to external research papers. There is no active development or community support associated with this repository.

Licensing & Compatibility

The repository itself is licensed under the MIT License. However, it links to research papers, each with its own copyright and distribution terms. Users must adhere to the licensing of the linked papers.

Limitations & Caveats

The collection is a curated list of papers and does not provide code implementations or interactive tutorials. Some links may become outdated or inaccessible over time. The organization is noted as needing improvement.