AI Atlas
LEARNING PATH

Learn AI from zero — in the right order.

The terms are laid out in a reading order — each section builds on the previous. Start anywhere; reading time is shown next to each section.

89 terms ~173 min total
Step 1

Foundations

5 terms · ~9 min total

The big picture first: what is AI, how does ML fit, why were neural networks a breakthrough, and how does a model actually 'run'?

  1. 1 Artificial Intelligence AI · ~2 min
  2. 2 Machine Learning ML · ~2 min
  3. 3 Neural Network Layered learning model · ~2 min
  4. 4 Inference The model's runtime phase · ~2 min
  5. 5 Computer Vision Machine seeing · ~1 min
Step 2

Machine Learning · In Depth

28 terms · ~55 min total

The three ML styles, classical algorithms, honest evaluation, and the basics of training neural networks — in order.

Core

  1. 1 Supervised Learning Learning from labeled examples · ~2 min
  2. 2 Unsupervised Learning Finding structure without labels · ~2 min
  3. 3 Reinforcement Learning RL · Learning by reward · ~2 min
  4. 4 Classification Assigning a label from a fixed set · ~2 min
  5. 5 Regression Predicting a continuous number · ~2 min
  6. 6 Clustering Grouping by similarity, no labels · ~2 min

Algorithms

  1. 7 Linear Regression Fitting a straight line · ~2 min
  2. 8 Logistic Regression Linear classifier with calibrated probabilities · ~2 min
  3. 9 Decision Tree Recursive yes/no splits · ~2 min
  4. 10 Random Forest An ensemble of decision trees · ~2 min
  5. 11 Gradient Boosting Sequentially correcting trees · ~2 min
  6. 12 Support Vector Machine SVM · Maximum-margin classifier · ~2 min
  7. 13 k-Nearest Neighbors k-NN · Predict by your neighbors · ~2 min
  8. 14 K-means Partition data into K clusters · ~2 min
  9. 15 Naive Bayes Probabilistic classifier with the independence trick · ~2 min

Evaluation

  1. 16 Overfitting Memorizing instead of learning · ~1 min
  2. 17 Bias-Variance Trade-off The decomposition of error · ~2 min
  3. 18 Cross-Validation Honest performance estimation · ~2 min
  4. 19 Loss Function What the model optimizes · ~2 min
  5. 20 Precision and Recall Two complementary metrics · ~2 min
  6. 21 Confusion Matrix Where the model gets it right and wrong · ~2 min
  7. 22 ROC and AUC Threshold-independent classifier ranking · ~2 min

Deep Learning

  1. 23 Backpropagation Backprop · Computing gradients across layers · ~2 min
  2. 24 Gradient Descent The optimizer underneath everything · ~2 min
  3. 25 Optimizer Adam, AdamW, SGD — what makes training work · ~2 min
  4. 26 Activation Function Where neurons get their nonlinearity · ~2 min
  5. 27 Dropout Random neuron deactivation · ~2 min
  6. 28 Batch Normalization Stabilizing layer activations · ~2 min
Step 3

Language Models

5 terms · ~10 min total

The core of modern AI: LLMs, their architecture (Transformer), the atomic unit (token), and the context they can see.

  1. 1 LLM Large Language Model · ~2 min
  2. 2 Transformer The architecture behind LLMs · ~2 min
  3. 3 Token The atomic unit of text for an LLM · ~2 min
  4. 4 Context Window How much an LLM can see at once · ~2 min
  5. 5 Multimodal Many modes, one model · ~2 min
Step 4

Talking to LLMs

9 terms · ~18 min total

The craft of steering a model: prompts, system messages, sampling parameters, and learning by example.

  1. 1 Prompt The instruction you give an LLM · ~2 min
  2. 2 System Prompt The persistent instruction · ~2 min
  3. 3 Temperature The randomness dial · ~2 min
  4. 4 Top-p Nucleus Sampling · ~2 min
  5. 5 Top-k Top-k Sampling · ~2 min
  6. 6 max_tokens Output length cap · ~2 min
  7. 7 Few-shot Learning Teaching by example · ~2 min
  8. 8 Chain-of-Thought CoT — Step-by-Step Reasoning · ~2 min
  9. 9 Streaming Token-by-token output · ~2 min
Step 5

Limits & Risks

4 terms · ~8 min total

Failure modes, security gaps, and the safety layers you must add in production.

  1. 1 Hallucination Confident wrong answers · ~2 min
  2. 2 Jailbreak Bypassing safety guardrails · ~2 min
  3. 3 Prompt Injection Hidden commands in user data · ~2 min
  4. 4 Guardrails Production safety filters · ~2 min
Step 6

Vectors & Meaning

3 terms · ~4 min total

The infrastructure of RAG: meaning as numbers, vector databases.

  1. 1 Vector Numeric representation · ~1 min
  2. 2 Embedding Meaning represented as numbers · ~2 min
  3. 3 Vector Database Similarity search store · ~1 min
Step 7

Custom Knowledge with RAG

7 terms · ~13 min total

Giving a model external knowledge without retraining — RAG's full stack and advanced variants.

  1. 1 RAG Retrieval-Augmented Generation · ~2 min
  2. 2 Chunking Splitting documents for retrieval · ~2 min
  3. 3 Hybrid Search Keyword + semantic, combined · ~1 min
  4. 4 BM25 Best Matching 25 — classic relevance score · ~2 min
  5. 5 Reranker The second-pass ranker · ~2 min
  6. 6 Knowledge Graph Structured knowledge as nodes + edges · ~2 min
  7. 7 RAG-Fusion Multi-query + fused ranking · ~2 min
Step 8

Agents & Tools

3 terms · ~6 min total

Going from chat to action: tool use, agents, the MCP standard.

  1. 1 Function Calling Tool Use · ~2 min
  2. 2 AI Agent An LLM that takes actions · ~2 min
  3. 3 MCP Model Context Protocol · ~2 min
Step 9

Training & Optimization

7 terms · ~14 min total

How models are trained, aligned, compressed, and evaluated.

  1. 1 Fine-tuning Specializing a pretrained model · ~2 min
  2. 2 RLHF Reinforcement Learning from Human Feedback · ~2 min
  3. 3 Alignment Tuning models to human values · ~2 min
  4. 4 LoRA Low-Rank Adaptation · ~2 min
  5. 5 Quantization Model compression · ~2 min
  6. 6 Knowledge Distillation Teacher → Student transfer · ~2 min
  7. 7 Benchmark Standardized evaluation · ~2 min
Step 10

Running Models

4 terms · ~8 min total

Running models efficiently in production / locally — runtime parameters and memory management.

  1. 1 num_ctx Runtime context length parameter · ~2 min
  2. 2 KV Cache Key-Value Cache · ~2 min
  3. 3 n_gpu_layers GPU layer offload count · ~2 min
  4. 4 Batch Size Sequences processed in parallel · ~2 min
Step 11

Advanced & Generative

9 terms · ~18 min total

Frontier topics: reasoning, MoE, and modern generative architectures (image, audio, video).

  1. 1 Reasoning Model Models that think first · ~2 min
  2. 2 Mixture of Experts MoE · ~2 min
  3. 3 Diffusion Model Generation by gradual denoising · ~2 min
  4. 4 GAN Generative Adversarial Network · ~2 min
  5. 5 Image Generation Text-to-image · ~2 min
  6. 6 TTS Text-to-Speech · ~2 min
  7. 7 ASR Automatic Speech Recognition · ~2 min
  8. 8 World Model An internal simulator inside the model · ~2 min
  9. 9 AGI Artificial General Intelligence · ~2 min
Step 12

Applied AI

5 terms · ~10 min total

The AI/ML applications most products actually ship: forecasting, anomaly detection, recommendations, sentiment.

  1. 1 Forecasting Predicting future values · ~2 min
  2. 2 Time Series Ordered observations over time · ~2 min
  3. 3 Anomaly Detection Spotting the unusual · ~2 min
  4. 4 Recommendation System Personalization at scale · ~2 min
  5. 5 Sentiment Analysis Inferring tone from text · ~2 min