My AI Notes #

Learning how machines learn! My working notes as I learn AI.

flowchart LR
    AI[Artificial Intelligence]
    ML[Machine Learning]
    DL[Deep Learning]
    FM[Foundation Models]
    LLM[LLM Models]

    AI --> ML
    ML --> DL
    DL --> FM
    FM --> LLM

    style AI fill:#E1F5FE
    style ML fill:#C8E6C9
    style DL fill:#90CAF9
    style FM fill:#64B5F6
    style LLM fill:#FFCCBC

• Mathematical Foundations for Machine Learning
• Statistical Methods
• Machine Learning
• Deep Neural Networks

• AI Foundation
  • AI Stages: ANI, AGI, ASI
  • AI Stack
  • AI Pipeline
  • AI Learning Resources
• Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Semi-Supervised Learning
  • Reinforcement Learning
  • Machine learning Workflow
  • Linear models for Regression
  • Linear models for classification
  • Decision Tree
  • Instance-based Learning
  • Support Vector Machine (SVM)
  • Bayesian Learning
  • Ensemble Learning
  • ML Model Evaluation/Comparison
• Generative AI
  • Foundation Models
  • LLM - Model
  • AI Agents
  • Retrieval-Augmented Generation (RAG)
• Deep Learning
  • Neural Networks
  • Perceptron
  • Transformer
• Mathematical Foundation
  • Linear Algebra
    • Linear Systems
      • Systems of Linear Equations
      • Matrices
      • Matrix Transposition
      • Solving Linear Systems
      • Inverse Matrix
      • Convex Combination
    • Vector Spaces
      • Linear Independence
      • Basis and Rank
      • Norm
      • Inner Products
      • Lengths and Distances
      • Angles and Orthogonality
      • Orthonormal Basis
      • Feature Space
    • Matrix Decompositions
      • Special Matrices
      • Determinant and Trace
      • Eigenvalues and Eigenvectors
      • Diagonalization
      • Cholesky Decomposition
      • Eigen Decomposition
      • Singular Value Decomposition (SVD)
      • Matrix Approximation
    • Dimensionality reduction and PCA
      • Principal Component Analysis (PCA)
      • PCA Theory
      • PCA in Practice
      • Latent Variable Perspective
      • Mathematical Preliminaries of SVM
      • Nonlinear SVM and Kernels
  • Calculus
    • Vector Calculus
      • Differentiation of Univariate Functions
      • Partial Differentiation and Gradients
      • Gradients of Vector-Valued and Matrix Functions
      • Useful Gradient Identities
      • Backpropagation and Automatic Differentiation
    • Continuous Optimisation
      • Optimisation using Gradient Descent
      • Constrained Optimisation
      • Lagrange Multipliers
      • Convex Optimisation
    • Nonlinear Optimisation
      • Challenges in Gradient-Based Optimisation
      • Stochastic Gradient Descent (SGD)
      • Momentum-Based Learning
      • Adaptive Methods: AdaGrad, RMSProp, Adam
      • Tuning Hyperparameters and Preprocessing
• Statistical Methods
  • Basic Statistics
  • Basic Probability
  • Conditional Probability & Bayes theorem
  • Probability Distributions
  • Hypothesis Testing
  • Prediction & Forecasting
  • Gaussian Mixture model & Expectation Maximization

• Machine Learning → The broad field where systems learn patterns from data to make predictions or decisions.
• Neural Networks → A subset of machine learning that uses interconnected artificial neurons to model complex relationships.
• Deep Learning → A subset of neural networks that uses many hidden layers to learn high-level features from large datasets.
• Foundation Models → Large deep learning models trained on massive datasets and reused across many tasks using transfer learning.
• LLMs (Large Language Models) → A specialised type of foundation model focused on understanding and generating human language.

flowchart TD

    %% Core hierarchy
    AI[Artificial Intelligence]
    ML[Machine Learning]
    NN[Neural Networks]
    DL[Deep Learning]
    FM[Foundation Models]
    LLM[LLM Models]

    %% Relationships
    AI --> ML
    ML --> NN
    NN --> DL
    DL --> FM
    FM --> LLM

    %% ML examples
    ML --> LR[Linear Regression]
    ML --> DT[Decision Trees]

    %% NN examples
    NN --> MLP[MLP]
    NN --> CNN[CNN]

    %% DL examples
    DL --> CNN_DL[CNN Deep]
    DL --> RNN[RNN]

    %% Foundation Model examples
    FM --> BERT[BERT]
    FM --> CLIP[CLIP]

    %% LLM examples
    LLM --> GPT[GPT]
    LLM --> LLAMA[LLaMA]

    %% Modalities
    LLM --> TEXT[Text]
    LLM --> IMAGE[Images]
    LLM --> AUDIO[Audio]
    LLM --> VIDEO[Video]

    %% Styling
    style AI fill:#E1F5FE
    style ML fill:#C8E6C9
    style NN fill:#BBDEFB
    style DL fill:#90CAF9
    style FM fill:#64B5F6
    style LLM fill:#FFCCBC

    style LR fill:#DCEDC8
    style DT fill:#DCEDC8

    style MLP fill:#E3F2FD
    style CNN fill:#E3F2FD

    style CNN_DL fill:#BBDEFB
    style RNN fill:#BBDEFB

    style BERT fill:#B3E5FC
    style CLIP fill:#B3E5FC

    style GPT fill:#FFE0B2
    style LLAMA fill:#FFE0B2

    style TEXT fill:#FFF9C4
    style IMAGE fill:#FFF9C4
    style AUDIO fill:#FFF9C4
    style VIDEO fill:#FFF9C4