A curated collection of AI/ML research paper analyses and implementations, documenting a comprehensive journey through the foundational and cutting-edge papers that define modern artificial intelligence.
This repository serves as a comprehensive knowledge base documenting my journey through seminal research papers in Artificial Intelligence and Machine Learning. For each paper, I provide:
- Deep Analysis: Detailed breakdowns of methodologies, contributions, and implications
- Critical Insights: Personal observations, strengths, weaknesses, and connections to other work
- Practical Implementations: Code implementations where applicable, translating theory to practice
- Historical Context: Understanding how each paper fits into the broader evolution of AI
Research-Analysis-Implementation/
├── README.md # This file
├── PAPER_ANALYSIS_TEMPLATE.md # Template for paper analysis
└── [Paper-Name]/ # One folder per paper
├── README.md # Analysis of the paper
├── implementation/ # Implementation code (if applicable)
This repository covers research across multiple AI subfields:
- Neural Networks & Deep Learning: From the McCulloch-Pitts neuron to modern deep architectures
- Computer Vision: CNNs, image recognition, and visual perception systems
- Natural Language Processing: From word embeddings to large language models
- Reinforcement Learning: TD-learning, DQN, and game-playing agents
- Generative Models: VAEs, GANs, and creative AI systems
- Artificial General Intelligence: The pursuit of human-level machine intelligence
- GitHub: @anirudhsengar
- LinkedIn: Anirudh Sengar
- Email: anirudhsengar3@gmail.com