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Grouped by section, in the order they build on each other. Open a topic to see its pages.
Foundations
Getting Started
1
How to Use This Site
CS Fundamentals for AI Engineers
7
CS Fundamentals for AI Engineers — Overview
CS Fundamentals for AI Engineers — Roadmap
Operating Systems & Concurrency
Networking & Distributed Systems
APIs, HTTP & Communication Patterns
Linux, Git & Developer Tooling
Software Engineering Practice
Python Engineering for AI
5
Python Engineering for AI — Overview
Python Engineering for AI — Roadmap
Language Fundamentals: OOP, Functional & Modern Python
Concurrency, Memory & Performance
Packaging, Testing & Tooling
Mathematics for AI
6
Mathematics for AI Overview
Mathematics for AI — Roadmap
Linear Algebra for AI
Calculus & Optimization for AI
Probability & Statistics for AI
Algorithms & Data Structures for AI
Models
Machine Learning
33
Machine Learning Overview
Machine Learning — Roadmap
ML Workflow Fundamentals, In Full Depth
Supervised Learning
Linear Regression, In Full Depth
Ridge Regression, In Full Depth
Lasso Regression, In Full Depth
Elastic Net, In Full Depth
Logistic Regression, In Full Depth
Unsupervised Learning
Decision Trees, In Full Depth
Random Forest & Extra Trees, In Full Depth
Boosting: AdaBoost & Gradient Boosting, In Full Depth
XGBoost, LightGBM & CatBoost, In Full Depth
Support Vector Machines (SVM & SVR), In Full Depth
K-Nearest Neighbors, In Full Depth
Naive Bayes, LDA & QDA, In Full Depth
SGD Classifier & Regressor, In Full Depth
Survival Analysis: Kaplan-Meier & Cox Proportional Hazards, In Full Depth
Time Series Forecasting: ARIMA, SARIMA & Prophet, In Full Depth
Model Evaluation & Metrics
Hyperparameter Optimization
Uncertainty Estimation & Conformal Prediction
K-Means & Hierarchical Clustering, In Full Depth
DBSCAN & HDBSCAN, In Full Depth
Gaussian Mixture Models & Spectral Clustering, In Full Depth
PCA, Kernel PCA & Truncated SVD, In Full Depth
ICA, t-SNE & UMAP, In Full Depth
Association Rule Mining: Apriori & FP-Growth, In Full Depth
Anomaly Detection: Isolation Forest, LOF & One-Class SVM, In Full Depth
Topic Modeling: LDA & BERTopic, In Full Depth
Recommender Systems, In Full Depth
Learning-to-Rank, In Full Depth
Deep Learning
20
Deep Learning Overview
Deep Learning — Roadmap
Neural Network Fundamentals
Activation Functions, In Full Depth
Loss Functions, In Full Depth
Optimizers, In Full Depth
Weight Initialization, Regularization & LR Scheduling
Training Deep Networks, In Full Depth
Convolutional Neural Networks, In Full Depth
Sequence Models
Attention & Transformers
BERT: Bidirectional Encoder Representations from Transformers
Linear Attention & State-Space Models (Mamba)
From RNNs to Transformers to Recurrent Looped Transformers
Vision Architectures: Transformers, Detection & Segmentation
Autoencoders & Variational Autoencoders, In Full Depth
Generative Models: GANs & Diffusion
GNNs, RL Networks, Metric Learning, SSL & Multimodal Nets
NN Layers Reference
Continual Learning
Computer Vision
5
Computer Vision — Overview
Computer Vision — Roadmap
Vision Fundamentals
Vision Tasks & Models
Modern Vision & Multimodal
NLP
5
NLP — Overview
NLP — Roadmap
Classical NLP: Tokenization, POS Tagging, NER & Parsing
Word Embeddings
NLP Task Taxonomy
Speech & Audio AI
4
Speech & Audio AI — Overview
Speech & Audio AI — Roadmap
Audio Fundamentals
Speech & Audio Tasks
LLMs & GenAI
10
LLMs & GenAI Overview
LLMs & GenAI — Roadmap
Foundation Model & Transformer Internals
Training Pipeline
Prompt Engineering
Calling the LLM API in Practice
Retrieval-Augmented Generation (RAG)
Retrieval & Reranking Architectures: Bi-Encoder, Cross-Encoder, Late Interaction
LLM Evaluation and Serving, In Full Depth
Multimodal & Generative Models
Graph ML
3
Graph ML — Overview
Graph ML — Roadmap
Graph ML Fundamentals
Reinforcement Learning
6
Reinforcement Learning — Overview
Reinforcement Learning — Roadmap
RL Fundamentals
Advanced RL
RL Environment & Reward Engineering
Multi-Armed & Contextual Bandits
Agents & Applications
Agents
19
A2A (Agent-to-Agent)
Agents Overview
Model Context Protocol (MCP), In Full Depth
Agents — Roadmap
MCP Protocol Deep Dive, In Full Depth
Agent Fundamentals, In Full Depth
Context Engineering
Agent Architectures
ReAct Agents in Production
Plan-and-Execute
Reflection / Self-Critique
Loops and Graphs: The Actual Anatomy
Routing & Supervisor Pattern
Single-Agent vs. Multi-Agent
Multi-Agent Systems
Agentic Coding Assistants
No-Code Agent Automation
Browser Automation Agents
Computer-Use & GUI Agents
AI for Science
3
AI for Science — Overview
AI for Science — Roadmap
AI for Science Fundamentals
Domain AI Applications
4
Domain AI Applications — Overview
Domain AI Applications — Roadmap
Healthcare AI
Other Domain Applications
Systems & Infrastructure
ML System Design
6
ML System Design Overview
ML System Design — Roadmap
The 9-Step ML System Design Framework
ML System Design Case Studies
Model Catalog & Benchmarking Framework
Technology Ecosystem Map
MLOps
32
MLOps Overview
MLOps — Roadmap
Engineering Foundations for ML
Data Engineering & Versioning
Big Data & Analytics
Experiment Tracking, In Full Depth
Pipeline Orchestration
Message Queues & Async Processing
Containers
APIs & Model Serving
Cloud Computing for ML
Deploying Models on AWS & Azure
Kubernetes
Infrastructure as Code
CI/CD & ML CI/CD
Feature Stores & Model Registry, In Full Depth
Deployment Strategies
Monitoring & Drift Detection
Observability
GPU/AI Infrastructure & Distributed Training
Federated Learning
LLM Inference Engines
LLM Inference Optimization
LLM Hosting, Serving Patterns & LLMOps Monitoring
LLM Evaluation & RAGOps
Security & Reproducibility
ML & LLM Testing
Production Reliability
AI Cost Engineering
Caching Strategies & Redis Patterns
Legal, Licensing & Governance
The Full MLOps Architecture, Priority Stack & Learning Path
Databases
7
Databases Overview
NoSQL Databases
Relational Databases (MySQL, Postgres)
Databases — Roadmap
PostgreSQL
Vector Databases
Graph Databases (Neo4j)
Frameworks
5
Frameworks Overview
Frameworks — Roadmap
Core ML/DL Frameworks
LLM / Agent Frameworks
Serving & MLOps / LLMOps
CLI Reference
9
CLI Reference Overview
CLI Reference — Roadmap
Linux & Shell Commands
Git Command Reference
Docker & Compose
kubectl Command Reference
Terraform Command Reference
Cloud CLIs: AWS, Azure, GCP
Python & JS Package Managers
Safety & Evaluation
AI Evaluation
5
AI Evaluation — Overview
AI Evaluation — Roadmap
Evaluation Fundamentals
LLM, RAG & Agent Evaluation
Human & Adversarial Evaluation
AI Security
5
AI Security — Overview
AI Security — Roadmap
OWASP LLM Top 10 & Prompt Attacks
Model & Data Attacks
AI Red Teaming & Adversarial Testing
AI Safety & Alignment
4
AI Safety & Alignment — Overview
AI Safety & Alignment — Roadmap
Alignment & RLHF
Scalable Oversight & Frontier Safety
Interpretability
4
Interpretability — Overview
Interpretability — Roadmap
Classical Interpretability: Feature Importance, SHAP & LIME
Deep Learning & LLM Interpretability
Research & Build
Research Engineering
4
Research Engineering — Overview
Research Engineering — Roadmap
How to Read AI Papers
The Paper Timeline
Build From Scratch
3
Build From Scratch — Overview
Build From Scratch — Roadmap
The Build List
Projects
4
Projects — Overview
Projects — Roadmap
The Project Ladder
Problem Statement Atlas
Visual Lab
17
Visual Lab — Overview
Visual Lab — Roadmap
Gradient Descent Explorer
Neural Network Playground
Attention Step-Through
RAG Pipeline Simulator
Agent Execution Graph
LLM Inference Flow Visualizer
Decision Boundary Playground
Embedding Space Explorer
Linear Regression Studio
Logistic Regression Studio
Ridge Regression Studio
Lasso Regression Studio
Two Pointers & Sliding Window, Step by Step
Algorithm Selector
Graph & Tree Traversal, Step by Step
Career
Interview Prep
13
Interview Prep Overview
Interview Prep — Roadmap
General Coding (DSA)
ML Coding
ML/GenAI Knowledge Q&A
Interview Q&A Quick Reference
RAG Interview Questions — The Full Bank
Classical ML Interview Questions
Deep Learning Training Interview Questions
LLM Serving and Fine-Tuning Interview Questions
Technology Comparisons & Decision Trees
System Design Practice
Behavioral
Roadmaps
3
The Neural Mastery Roadmap
Suggested Paths
AI Engineering Track
Resources
1
Open Source AI Resources