1. 1. Overview
  2. Summary
  3. 2. Table of Contents
  4. Generative AI
  5. 3. What is Generative AI?
  6. 4. Machine Learning
    1. 4.1. Deep Learning
    2. 4.2. Neural Networks
      1. 4.2.1. Neuron
      2. 4.2.2. Perceptron
      3. 4.2.3. Weights
      4. 4.2.4. Features
      5. 4.2.5. Bias
      6. 4.2.6. Classification
      7. 4.2.7. Regression
        1. 4.2.7.1. Linear Regression
        2. 4.2.7.2. Polynomial Regression
        3. 4.2.7.3. Ridge Regression
        4. 4.2.7.4. Lasso Regression
        5. 4.2.7.5. Logistic Regression
      8. 4.2.8. Multi-Layer Perceptron
      9. 4.2.9. Generative Adversarial Networks
  7. Models
  8. 5. What is a Model?
    1. 5.1. LLM
    2. 5.2. Datasets
    3. 5.3. VAE
    4. 5.4. Training
    5. 5.5. Inference
    6. 5.6. Evaluation
    7. 5.7. Decision Trees
    8. 5.8. Support Vector Machines
  9. RAG
  10. 6. What is RAG?
  11. 7. Embedders

Notes about AI/LLM/RAG

Introduction

  • Overview

Summary

  • Table of Contents

Generative AI

  • What is Generative AI?
  • Machine Learning
    • Deep Learning
    • Neural Networks
      • Neuron
      • Perceptron
      • Weights
      • Features
      • Bias
      • Classification
      • Regression
        • Linear Regression
        • Polynomial Regression
        • Ridge Regression
        • Lasso Regression
        • Logistic Regression
      • Multi-Layer Perceptron
      • Generative Adversarial Networks

Models

  • What is a Model?
    • LLM
    • Datasets
    • VAE
    • Training
    • Inference
    • Evaluation
    • Decision Trees
    • Support Vector Machines

RAG

  • What is RAG?
  • Embedders