The Grand AI Handbook

GitHub

The Grand AI Handbook

The Grand AI Handbook

GitHub

AI Handbooks ▶

Handbooks Home

Top AI Fields ▶

Generative AI ▶

Overview I: Foundation: Statistical Prediction and ML II: Neural Networks and Deep Learning III: LLM Architecture and Training IV: Finetuning and Alignment V: Applications and Interpretability VI: Inference Optimization VII: Addressing the Quadratic Scaling Problem VIII: Beyond Transformers: Other Generative Models IX: Multimodal Models

Foundation Models ▶

Overview I: Foundation Models and Their Applications II: NLP and Computer Vision III: RNN and CNN IV: Early Transformer Variants V: Self-Attention and Transformers VI: Efficient Transformers VII: Parameter-Efficient Tuning VIII: Language Model Pretraining IX: Large Language Models X: Scaling Law XI: Instruction Tuning and RLHF XII: Efficient LLM Training XIII: Efficient LLM Inference XIV: Compress and Sparsify LLM XV: LLM Prompting XVI: Vision Transformers XVII: Diffusion Models XVIII: Image Generation XIX: Multimodal Pretraining XX: Large Multimodal Models XXI: Tool Augmentation XXII: Retrieval Augmentation

Computer Vision ▶

Overview I: Mathematical and Statistical Foundations II: Core Concepts and Traditional Methods III: Deep Learning Foundations for Vision IV: CNN Architectures and Enhancements V: Core and Extended Vision Tasks VI: Advanced Learning Paradigms VII: Vision Transformers and Large-Scale Models VIII: 3D and Geometric Vision IX: Generative Vision Models X: Multimodal and Dynamic Vision XI: Efficiency and Optimization XII: Evaluation and Applications XIII: Deployment, Ethics, and Future Directions XIV: Summary of Key Concepts XV: Resources for further learning XVI: Glossary XVII: References

Reinforcement Learning ▶

Overview I: Mathematical and Statistical Foundations II: Core Concepts of Reinforcement Learning III: Classical RL Algorithms IV: Deep Reinforcement Learning Foundations V: Advanced RL Paradigms VI: Multi-Agent and Game-Theoretic RL VII: RL with Human Interaction VIII: Exploration and Representation Learning in RL IX: Transformers in RL X: Alignment and Reasoning with Transformers XI: RL for Sequential and Structured Tasks XII: Scalability and Efficiency in RL XIII: Evaluation and Benchmarking XIV: Applications of RL XV: Deployment, Ethics, and Future Directions XVI: Summary of Key Concepts XVII: Resources for Further Learning XVIII: Glossary XIX: References

Core AI Fields ▶

Machine Learning Deep Learning

Natural Language Processing ▶

Overview I: Linguistic and Computational Foundations II: Traditional NLP Techniques III: Statistical NLP Methods IV: Neural Networks for NLP V: Word Embeddings and Representations VI: Transformer Models VII: Pretraining and Scaling VIII: Exploration in NLP IX: Finetuning and Adaptation X: Alignment and Ethics XI: Multilingual NLP XII: Complex NLP Tasks XIII: Multimodal NLP XIV: Efficiency and Deployment XV: Evaluation and Benchmarks XVI: Applications and Future Directions

Large Language Models

MLOps ▶

Overview I: Foundations of MLOps II: Data Management for MLOps III: Model Development IV: Model Deployment V: Monitoring and Maintenance VI: Scalability and Optimization VII: Collaboration and Workflow VIII: Ethics and Responsible AI IX: MLOps for Specialized Domains X: Large Language Model Operations (LLMOps) XI: Advanced MLOps Techniques XII: Multimodal and Cross-Disciplinary MLOps XIII: Evaluation and Benchmarking XIV: MLOps Tools and Ecosystems XV: Industry Applications XVI: Future Directions in MLOps

AI Domains ▶

Explainable AI ▶

Overview I: Foundations of Interpretability II: Interpretable Models III: Local Model-Agnostic Methods IV: Global Model-Agnostic Methods V: Neural, Generative, and RL Model Interpretability VI: Practical Considerations VII: Beyond the Methods

Federated Learning Multimodal AI Recommender Systems Robotics & AI Self-Driving Cars Audio AI

Specialized AI Techniques ▶

Bayesian Machine Learning Efficient AI & Optimization Graph Neural Networks Representation Learning Self-Supervised Learning Time Series Forecasting

Applied AI & Ethics ▶

AI Ethics & Fairness AI for Art & Aesthetics AI for Edge & IoT AI in Finance AI in Healthcare

Ecosystem ▶

Ecosystem Home Models Tools Benchmarks

Competitions ▶

Active Competitions Past Competitions

Research ▶

Research Home

AI Spotlight ▶

AI News AI Trends

Research Highlights ▶

Breakthroughs Weekly Papers Landmark Papers

Deep Dive & Challenges ▶

Reviews and Surveys Open Challenges Reproducibility

Career Paths ▶

Career Paths Home ML Engineer Data Scientist AI Researcher

Resources ▶

Resources Home AI Tools & Libraries Datasets Books & Papers Tutorials

About ▶

About Project Team Contact How to Contribute Code of Conduct

Summary of Key Concepts

Summary of Key Concepts