Learn: the AI Roadmap and Encyclopedia as a feed
Everything on the AI Roadmap and in the AI Encyclopedia, in learning order.
Phase 1: Foundations
- What is AI? · The Big Picture (13 sources)
Phase 2: Python & Math
- Python Programming for AI (15 sources)
- Math for AI · Linear Algebra & Calculus (13 sources)
- Statistics & Probability for ML (13 sources)
- NumPy, Pandas & Data Visualization (13 sources)
Phase 3: Machine Learning
- Data Engineering & Data Pipelines for AI (13 sources)
- Intro to Machine Learning (14 sources)
- Model Evaluation & Experiment Tracking (13 sources)
- Scikit-Learn & Hands-On ML Projects (12 sources)
- 🧪 PRACTICE MILESTONE: Build Your First ML Project (12 sources)
Phase 4: Deep Learning
- Neural Networks & Deep Learning Fundamentals (13 sources)
- PyTorch · The Deep Learning Framework (14 sources)
- CNNs & Computer Vision (13 sources)
- RNNs, LSTMs & Sequence Models (11 sources)
- Transformers & Attention Mechanism (15 sources)
- 🏗️ PRACTICE MILESTONE: Deep Learning Projects (11 sources)
Phase 5: NLP & LLMs
- NLP & Text Processing Fundamentals (14 sources)
- Prompt Engineering & Using LLMs Effectively (14 sources)
- Large Language Models (LLMs) · GPT, BERT & Beyond (18 sources)
- Fine-Tuning & RAG · Customizing LLMs (16 sources)
- 🚀 PRACTICE MILESTONE: Build & Deploy an LLM App (14 sources)
Phase 6: Specialize & Build
- Generative AI · Images, Audio & Video (14 sources)
- AI Agents & Agentic AI (18 sources)
- MLOps & Deploying AI Models (13 sources)
- Reinforcement Learning Deep Dive (15 sources)
- Build Your AI Portfolio & Get Hired (12 sources)
- AI Safety, Ethics & Responsible AI (13 sources)
- Reading Research Papers & Staying Current (13 sources)
Phase 7: Depth Tracks
- Multimodal AI & Vision-Language Models (11 sources)
- Speech & Audio AI (8 sources)
- Robotics & Embodied AI (6 sources)
- Efficient AI · Quantization & Fast Inference (8 sources)
- Recommender Systems (5 sources)
- Time Series & Forecasting (6 sources)
- Graph Machine Learning (6 sources)
Core ML Concepts
- Artificial Intelligence (AI) (8 sources)
- Machine Learning (ML) (7 sources)
- Supervised Learning (4 sources)
- Unsupervised Learning (4 sources)
- Neural Network (5 sources)
- Deep Learning (7 sources)
- Features & Labels (3 sources)
- Overfitting & Underfitting (4 sources)
- Bias-Variance Tradeoff (3 sources)
- Classification vs Regression (3 sources)
- Transfer Learning (5 sources)
- Training vs Inference (4 sources)
- Regularization (3 sources)
- Few-Shot & Zero-Shot Learning (3 sources)
- Linear Regression (5 sources)
- Semi-Supervised Learning (4 sources)
- Time Series Forecasting (3 sources)
- Data · The Fuel of AI (3 sources)
- Decision Trees & Random Forests (5 sources)
- Support Vector Machines (SVM) (4 sources)
- Evaluation Metrics & Cross-Validation (5 sources)
- Self-Supervised Learning (5 sources)
- Continual Learning & Catastrophic Forgetting (3 sources)
- Meta-Learning (Learning to Learn) (3 sources)
- Causal Inference & ML (3 sources)
- Symbolic AI & Expert Systems (3 sources)
- Logistic Regression (5 sources)
- Data Leakage (4 sources)
- Exploratory Data Analysis (EDA) (4 sources)
- Data Augmentation (6 sources)
- k-Nearest Neighbors (kNN) (4 sources)
- Naive Bayes (4 sources)
- Data Science vs ML vs AI (4 sources)
- Narrow AI vs Strong AI (6 sources)
Math & Optimization
- Gradient Descent (3 sources)
- Backpropagation (4 sources)
- Loss Function (4 sources)
- Activation Functions (4 sources)
- Embeddings (5 sources)
- Dimensionality Reduction (4 sources)
- Feature Engineering (5 sources)
- K-Means Clustering (4 sources)
- Anomaly Detection (5 sources)
- Boosting (XGBoost, LightGBM) (5 sources)
- Linear Algebra for ML (5 sources)
- Probability & Bayes' Theorem (5 sources)
- Information Theory: Entropy & KL Divergence (2 sources)
- Optimizers: SGD, Momentum & Adam (3 sources)
- Bayesian ML & Uncertainty (3 sources)
- Monte Carlo Methods (5 sources)
- Probabilistic Graphical Models & HMMs (3 sources)
- Evolutionary & Swarm Optimization (3 sources)
Architectures
- Transformer Architecture (5 sources)
- Attention Mechanism (4 sources)
- Self-Attention (4 sources)
- Convolutional Neural Network (CNN) (5 sources)
- Recurrent Neural Network (RNN) (4 sources)
- LSTM (Long Short-Term Memory) (4 sources)
- GPT (Generative Pre-trained Transformer) (6 sources)
- BERT (Bidirectional Encoder) (4 sources)
- State Space Models (Mamba) (4 sources)
- Vision Transformer (ViT) (5 sources)
- Multimodal Models (5 sources)
- Knowledge Distillation (4 sources)
- Model Quantization & Compression (3 sources)
- Neural Architecture Search (NAS) (4 sources)
- Neuro-Symbolic AI (4 sources)
- Mixture-of-Experts (MoE) (5 sources)
- Residual Connections & ResNet (4 sources)
- Normalization Layers (BatchNorm to RMSNorm) (4 sources)
- Autoencoders (3 sources)
- Graph Neural Networks (GNNs) (5 sources)
- Positional Encoding & RoPE (5 sources)
NLP & Language
- Natural Language Processing (NLP) (7 sources)
- Tokenization (4 sources)
- Word2Vec & Word Embeddings (4 sources)
- Prompt Engineering (6 sources)
- Retrieval-Augmented Generation (RAG) (7 sources)
- Fine-Tuning LLMs (5 sources)
- Chain-of-Thought Reasoning (5 sources)
- Vector Databases (4 sources)
- LLM Scaling Laws (4 sources)
- RLHF (Reinforcement Learning from Human Feedback) (5 sources)
- Context Window & Long Context (4 sources)
- Text Generation & Sampling (4 sources)
- Sentiment Analysis (6 sources)
- Machine Translation (4 sources)
- Named Entity Recognition (NER) (3 sources)
- Reasoning Models & Test-Time Compute (5 sources)
- Context Engineering (4 sources)
- Speech Recognition (ASR) & Whisper (4 sources)
- DPO & Preference Optimization (4 sources)
- LoRA & Parameter-Efficient Fine-Tuning (5 sources)
- Open-Weights & Small Language Models (5 sources)
- Bag-of-Words & TF-IDF (4 sources)
- Text Summarization (3 sources)
- Topic Modeling (4 sources)
- Information Extraction (4 sources)
- Large Language Models (LLMs) (5 sources)
Computer Vision
- Image Classification (4 sources)
- Object Detection (4 sources)
- Semantic Segmentation (3 sources)
- YOLO (You Only Look Once) (4 sources)
- Image Generation (5 sources)
- Neural Style Transfer (4 sources)
- Deepfakes & Face Synthesis (2 sources)
- Contrastive Learning & CLIP (4 sources)
- OCR & Document AI (5 sources)
- Face Detection & Recognition (5 sources)
Generative AI
- Synthetic Data (5 sources)
- Diffusion Models (5 sources)
- GANs (Generative Adversarial Networks) (5 sources)
- VAEs (Variational Autoencoders) (2 sources)
- Stable Diffusion & Text-to-Image (5 sources)
- Text-to-Video Generation (4 sources)
- Voice Cloning & TTS (2 sources)
- AI Music Generation (3 sources)
- AI Code Generation (4 sources)
- 3D Generation & NeRFs (3 sources)
- AI Image Editing & Inpainting (3 sources)
- Flow Matching & Modern Diffusion (4 sources)
Reinforcement Learning
- Reinforcement Learning (RL) (7 sources)
- Q-Learning & Deep Q-Networks (DQN) (4 sources)
- Policy Gradient & Actor-Critic (5 sources)
- PPO (Proximal Policy Optimization) (5 sources)
- Markov Decision Process (MDP) (4 sources)
- Reward Design & Reward Hacking (4 sources)
- Multi-Agent Reinforcement Learning (4 sources)
- World Models (4 sources)
- Sim-to-Real Transfer (3 sources)
- Inverse Reinforcement Learning (3 sources)
- GRPO (Group Relative Policy Optimization) (5 sources)
MLOps & Infrastructure
- GPU & TPU Computing (5 sources)
- Model Deployment & Serving (4 sources)
- MLOps (ML Operations) (6 sources)
- Experiment Tracking (4 sources)
- Data Pipelines & ETL (5 sources)
- Edge AI & On-Device ML (3 sources)
- Federated Learning (4 sources)
- Distributed Training (4 sources)
- Model Versioning & Registry (4 sources)
- ML Monitoring & Observability (3 sources)
- LLM Evaluation & Benchmarks (5 sources)
- Inference Optimization (4 sources)
- Hyperparameter Tuning (4 sources)
- The AI Stack (5 sources)
- Quantum Machine Learning (5 sources)
- AI Data Centers & the AI Factory (4 sources)
- Numerical Precision & Mixed-Precision Training (6 sources)
- ML Frameworks (PyTorch, TensorFlow, JAX) (7 sources)
- Inference Cost & Token Economics (7 sources)
- AI Platforms (7 sources)
AI Agents & Applications
- AI Agents (8 sources)
- Tool Use & Function Calling (6 sources)
- Autonomous Vehicles & Robotics (4 sources)
- Recommendation Systems (4 sources)
- AI-Driven Drug Discovery (3 sources)
- Explainable AI (XAI) (5 sources)
- AI-Powered Search & Ranking (3 sources)
- Conversational AI & Chatbots (5 sources)
- AI in Healthcare & Medical Imaging (4 sources)
- AI for Creative Industries (4 sources)
- Model Context Protocol (MCP) (5 sources)
- Computer Use & Browser Agents (3 sources)
- Agentic Coding (4 sources)
- Vision-Language-Action Models (4 sources)
- Multi-Agent Systems & Orchestration (4 sources)
- Agent Memory, Planning & Reflection (3 sources)
- Agent Harnesses & Scaffolding (3 sources)
- Agent-to-Agent Protocols (A2A) (7 sources)
- Enterprise AI (7 sources)
- Vibe Coding (7 sources)
Safety, Ethics & Governance
- AI Alignment (5 sources)
- AI Bias & Fairness (5 sources)
- AI Hallucinations (4 sources)
- Red Teaming AI Systems (3 sources)
- AI Regulation & Governance (5 sources)
- Prompt Injection & Jailbreaking (6 sources)
- Mechanistic Interpretability (6 sources)
- AI Watermarking & Content Detection (4 sources)
- Constitutional AI & RLAIF (3 sources)
- Differential Privacy & ML Privacy (4 sources)
- AGI (Artificial General Intelligence) (5 sources)
- AI Safety (6 sources)
- Superintelligence (ASI) (7 sources)
- Technological Singularity (7 sources)
- Responsible AI (7 sources)