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  • Textbook Content
  • Part 1Mathematical Foundations for Machine Learning
    • Ch 1: Linear Algebra for ML
    • Ch 2: Calculus and Optimization
    • Ch 3: Probability and Statistics
  • Part 2Python Ecosystem and Classical Machine Learning
    • Ch 4: Python Data Science Stack
    • Ch 5: Supervised Learning Fundamentals
    • Ch 6: Classification Algorithms
    • Ch 7: Ensemble Methods
    • Ch 8: Unsupervised Learning
  • Part 3Deep Learning Fundamentals
    • Ch 9: Neural Network Foundations
    • Ch 10: Training Deep Networks
    • Ch 11: Convolutional Neural Networks
    • Ch 12: Sequence Models
  • Part 4Modern Architectures and Large Language Models
    • Ch 13: Transformer Architecture
    • Ch 14: Pre-trained Language Models
    • Ch 15: Fine-tuning and Alignment
    • Ch 16: Efficient Transformer Architectures
    • Ch 17: Vision Transformers
  • Part 5Generative Models
    • Ch 18: Variational Autoencoders
    • Ch 19: Generative Adversarial Networks
    • Ch 20: Diffusion Models
  • Part 6Multimodal AI and Vision-Language Models
    • Ch 21: Vision-Language Foundations
    • Ch 22: Text-to-Image and Beyond
  • Part 7Applied AI Systems
    • Ch 23: Retrieval-Augmented Generation
    • Ch 24: Agentic AI Systems
    • Ch 25: Reinforcement Learning
    • Ch 26: MLOps and Production Systems
    • Ch 27: AI Ethics and Responsible Development
  • Part 8Data Science: Process & Practice
    • Ch 28: The Data Science Lifecycle
    • Ch 29: Data Acquisition & Wrangling
    • Ch 30: Exploratory Data Analysis
    • Ch 31: Feature Engineering & Selection
    • Ch 32: Data Visualization & Storytelling
  • Part 9Statistics & Statistical Inference
    • Ch 33: Descriptive Statistics & Distributions
    • Ch 34: Probability Distributions in Depth
    • Ch 35: Sampling & Estimation
    • Ch 36: Hypothesis Testing
    • Ch 37: Resampling & the Bootstrap
    • Ch 38: Bayesian Inference
    • Ch 39: Regression & Generalized Linear Models
    • Ch 40: Experimental Design & A/B Testing
  • Part 10Databases & SQL for Data Science
    • Ch 41: Relational Databases & SQL Fundamentals
    • Ch 42: Intermediate SQL: Joins & Aggregation
    • Ch 43: Advanced SQL: Window Functions & CTEs
    • Ch 44: Analytical SQL & Data Warehousing
    • Ch 45: NoSQL & Modern Data Stores
  • Part 11Time Series Analysis & Forecasting
    • Ch 46: Time Series Fundamentals
    • Ch 47: Classical Forecasting: ARIMA & ETS
    • Ch 48: Machine Learning for Time Series
  • Part 12Programming for Data Science
    • Ch 49: Python for Data Science in Depth
    • Ch 50: R & the Tidyverse
    • Ch 51: Reproducible Research & Notebooks
  • Appendices
  • Appendix A: Environment Setup Guide
  • Appendix B: GPU Computing Fundamentals
  • Appendix C: Cloud Platform Quick Start
  • Appendix D: Mathematics Reference
  • Appendix E: Library Quick Reference
  • Appendix F: Python / R / SQL Rosetta Stone
  • Appendix G: Statistics & Probability Formula Reference
  • Appendix H: SQL Recipes & Patterns
  • Appendix I: Data Science Interview Preparation
  • Resources
  • Library Reference
  • Learning Paths
  • Jupyter Notebooks
  • About
Home/Libraries/PyTorch

PyTorch

Deep Learning

Open source machine learning framework that accelerates the path from research to production.

Installation
pip install torch
Documentation
Official Docs

Chapters Using PyTorch

Chapter 9: Neural Network Foundations (Deep Learning Fundamentals)
Primary
Chapter 10: Training Deep Networks (Deep Learning Fundamentals)
Primary
Chapter 11: Convolutional Neural Networks (Deep Learning Fundamentals)
Primary
Chapter 12: Sequence Models (Deep Learning Fundamentals)
Primary
Chapter 13: Transformer Architecture (Modern Architectures and Large Language Models)
Primary
Chapter 16: Efficient Transformer Architectures (Modern Architectures and Large Language Models)
Primary
Chapter 17: Vision Transformers (Modern Architectures and Large Language Models)
Primary
Chapter 18: Variational Autoencoders (Generative Models)
Primary
Chapter 19: Generative Adversarial Networks (Generative Models)
Primary
Chapter 20: Diffusion Models (Generative Models)
Primary

© 2026 AI/ML Engineering Textbook

A comprehensive guide to AI/ML engineering, from mathematical foundations to agentic systems. Static build.