The mental map

Artificial Intelligence — Building systems that perceive, reason, learn and act.

• • Symbolic AIChapter 1

    Intelligence as explicit symbols, rules and search.

    • Symbolic AI
    • Logic & rules
      • Logic and Rules
    • Search & planning
      • Planning
      • Search
    • Knowledge representation
      • Knowledge Representation
    • Expert systems
      • Expert Systems
      • The Knowledge-Acquisition Bottleneck
• • Mathematical toolkitChapter 2

    The language every learning system is written in.

    • Linear algebra
      • Dot Product
      • Matrix Multiplication
      • Tensors and Shapes
      • Vectors
    • Probability
      • Conditional Probability and Bayes' Theorem
      • Probability of Sequences
      • Expected Value and Variance
      • Probability and Distributions
      • Softmax
    • Calculus
      • The Chain Rule
      • Derivatives and Gradients
    • Optimization
      • Gradient Descent
      • Loss Functions
      • Momentum and Adam
      • Stochastic Gradient Descent (SGD)
    • Statistics
      • Sampling and Uncertainty
    • Information theory
      • Cross-Entropy Loss
      • Entropy
      • KL Divergence
      • Perplexity
• • Machine LearningChapter 3

    Systems that improve at a task from data.

    • Supervised, Unsupervised and Self-Supervised Learning
    • From Rules to Learning
    • Classical MLChapter 3

      Regression, trees, SVMs, clustering — hand-crafted features.

      • Data Leakage
      • Decision Trees and Random Forests
      • Distribution Shift
      • Evaluation Metrics for Classifiers
      • Hand-Crafted Features vs Learned Features
      • Features, Labels and Tasks
      • Generalization, Overfitting and Underfitting
      • k-Means Clustering
      • Linear Regression
      • Logistic Regression
      • Naive Bayes
      • Principal Component Analysis (PCA)
      • Regularization
      • Support Vector Machines
    • Reinforcement learningChapter 5

      Learning from consequences and rewards.

      • Actor–Critic Methods
      • Deep RL, from Atari to AlphaGo to RLHF
      • Exploration vs Exploitation
      • MDPs, Policies and Value
      • Policy Gradients
      • Q-Learning
      • Reinforcement Learning
    • Deep LearningChapter 4

      Neural networks that learn their own features.

      • From MLPs to Transformers: The Architecture Story
      • Representation Learning
      • Neural nets & trainingChapter 4
        • Activation Functions
        • The Artificial Neuron
        • Backpropagation
        • Batch Normalization
        • Computational Graphs and Autodiff
        • Dropout
        • The Forward Pass
        • Weight Initialization
        • Multilayer Perceptron (MLP)
        • The Perceptron
        • Vanishing and Exploding Gradients
      • CNNsChapter 5
        • Convolutional Neural Networks
        • Convolution
        • ImageNet, AlexNet and ResNet
        • Images as Tensors
        • Pooling and Downsampling
      • RNNs & LSTMsChapter 6
        • LSTMs and GRUs
        • Recurrent Neural Networks
        • Sequence-to-Sequence Models
      • Language (NLP)Chapter 6
        • Language Modeling
        • N-Gram Models
        • One-Hot Encoding
        • Text as Data
        • Word embeddingsChapter 6
          • Embeddings
          • GloVe
          • Neural Language Model
          • Word2Vec
        • AttentionChapter 6
          • Attention
          • The Fixed-Vector Bottleneck
          • Neural Machine Translation
      • TransformersChapter 7
        • Self-attentionChapter 7
          • Causal Masking
          • Multi-Head Attention
          • Self-Attention
        • Transformer blockChapter 7
          • Feed-Forward Sublayer (MLP)
          • Layer Normalization
          • Positional Encoding
          • Residual Connections
          • The Transformer Block
        • Encoder / decoder variantsChapter 7
          • Encoder, Decoder & Encoder–Decoder
• • Foundation ModelsChapter 8

    Large models pretrained once and adapted to many tasks.

    • Language models (LLMs)Chapter 8
      • PretrainingChapter 8
        • Autoregressive Next-Token Prediction
        • Decoding: Greedy, Temperature, Top-k, Top-p
        • Emergent Abilities and the Debate
        • GPT-1 → GPT-2 → GPT-3
        • In-Context Learning
        • Parameters, Tokens and Context Windows
        • Why Next-Token Prediction Goes So Far
        • Pretrain, Then Fine-Tune
        • Pretraining at Scale
        • Tokenization
      • Data, scale & trainingChapter 9 · later
        • Scaling Laws (Preview)
      • Post-trainingChapter 10 · later
        • From Base Model to ChatGPT
      • Efficient architectures & inferenceChapter 11 · laterno pages yet
    • VisionChapter 5
      • Detection and Segmentation
      • Vision Transformer (ViT)
    • Audio & speechChapter 5
      • Audio and Spectrograms
      • Speech Recognition and Synthesis
    • Diffusion & generative mediaChapter 15 · laterno pages yet
    • MultimodalChapter 15 · laterno pages yet
• • AI SystemsChapter 12 · later

    Models embedded in larger loops with data and tools.

    • Embeddings & retrievalChapter 12 · laterno pages yet
    • RAGChapter 12 · laterno pages yet
    • Tools & structured outputChapter 12 · laterno pages yet
    • AgentsChapter 13 · laterno pages yet
    • Reasoning & test-time computeChapter 14 · laterno pages yet
• • Science of AIChapter 16 · later

    Measuring, trusting, securing and understanding models.

    • EvaluationChapter 16 · laterno pages yet
    • ReliabilityChapter 16 · laterno pages yet
    • Safety & securityChapter 16 · laterno pages yet
    • InterpretabilityChapter 16 · laterno pages yet
    • Research methodEpilogue · laterno pages yet