AI & Machine Learning¶
A comprehensive collection of technical reference articles covering the full AI/ML landscape — from classical algorithms to cutting-edge foundation models.
Categories¶
Foundations¶
Core theory and methodology that underpins all of machine learning.
| Article | Covers |
|---|---|
| Machine Learning Algorithms | Linear models, SVM, trees, ensembles, clustering |
| Neural Network Architectures | CNN, RNN, Transformer, GAN, Diffusion |
| Deep Learning Fundamentals | Activation functions, backpropagation, LSTM internals |
| Model Training & Optimization | Loss functions, optimizers, regularization, distributed training |
| Feature Engineering | Encoding, scaling, selection, feature stores |
| Model Evaluation Metrics | Classification, regression, ranking, calibration |
| Bayesian Machine Learning | Bayes theorem, GPs, BNNs, uncertainty quantification |
Applications¶
Domain-specific architectures and techniques.
| Article | Covers |
|---|---|
| Natural Language Processing | Tokenization, embeddings, BERT/GPT, RAG |
| Computer Vision | Detection, segmentation, pose, 3D vision, SAM |
| Time Series Forecasting | ARIMA, Prophet, PatchTST, foundation models |
| Graph Neural Networks | GCN, GAT, message passing, drug discovery |
| AI for Code | Code models, Copilot, SWE-bench, program repair |
| Multimodal AI | CLIP, LLaVA, text-to-image/video, cross-modal |
Advanced Topics¶
Frontier research and emerging paradigms.
| Article | Covers |
|---|---|
| Reinforcement Learning | MDP, DQN, PPO, RLHF, multi-agent |
| Generative AI & LLMs | GPT, MoE, fine-tuning, RAG, agents, safety |
| Transfer Learning & Domain Adaptation | Few-shot, MAML, domain shift, continual learning |
| Federated Learning | FedAvg, privacy, non-IID, FL for LLMs |
| AI Agents & Autonomous Systems | ReAct, multi-agent, robotics, safety |
Infrastructure¶
Engineering the systems that make AI work in production.
| Article | Covers |
|---|---|
| Data Engineering | Storage formats, OLTP/OLAP, databases |
| MLOps & Model Deployment | Serving, monitoring, drift, CI/CD for ML |
| AI Hardware & Accelerators | GPU, TPU, edge chips, interconnects, optimization |
Society & Ethics¶
The broader impact and responsible development of AI.
| Article | Covers |
|---|---|
| AI Ethics & Responsible AI | Bias, fairness, explainability, regulation |
| AI Impact 30-Year Outlook | Best/worst case scenarios, mitigation strategies |