Name: Mastering PyTorch and Lightning
Brand: Leanpub
Price: 5.00 USD
Availability: InStock

Training a neural network is only the beginning. Reliable deep learning systems also require reproducible data pipelines, correct gradient computation, efficient GPU utilization, structured experimentation, and code that remains understandable as projects grow.

Mastering PyTorch and Lightning is a practical, programming-oriented guide for students, researchers, ML practitioners, research engineers, and Python developers who want to create cleaner, more maintainable deep learning systems.

What Makes This Book Different?
Concise by design, this book delivers a focused engineering path without requiring you to work through more than 500 pages before writing useful code. Its value lies not in its word count, but in the time it saves by taking you directly from tensor internals and memory behavior to robust PyTorch and Lightning architectures. The course follows a pragmatic, code-along approach. Every component is constructed progressively through clear explanations, executable examples, guided checkpoints, and hands-on projects. From the first chapter, you will explore tensor architecture, memory allocation, shapes, devices, and gradient behavior before applying these foundations to complete training systems.

A Coding-First Course
This is not a traditional mathematics-heavy deep learning textbook. It does not focus on lengthy proofs or extensive graphical derivations. Theoretical concepts are introduced only when they are necessary to understand what the code is doing, why a model behaves in a particular way, and how an architecture should be designed.

The primary focus is programming:

writing clear and maintainable PyTorch code;
applying reliable deep learning design patterns;
detecting tensor, gradient, device, and data-pipeline errors;
building reusable training and evaluation workflows;
improving reproducibility and experiment structure;
preparing code for larger academic or production-facing projects;
making deep learning systems easier to test, scale, and maintain.

Gotchas, Reality Checks, and Q&A
Throughout the book, dedicated Gotcha / Reality Check sections expose common implementation mistakes and explain their practical consequences. Every chapter concludes with a focused Q&A knowledge assessment designed to reinforce essential concepts, reveal misconceptions, and develop the engineering expertise required to reason about real machine-learning systems. Two progressive capstone checkpoints allow you to apply the concepts in complete deep learning workflows.

Who Is This Book For?
This book is designed for readers who:

understand basic Python and want to begin deep learning with solid programming habits;
already use PyTorch but want to improve the structure of their code;
conduct academic research and require reproducible experiments;
need practical exposure to GPU execution, distributed training, and performance-oriented workflows.

About the Author
Aghiles Kebaili holds a PhD in Artificial Intelligence and specializes in deep learning and computer vision for medical imaging. He works as a Computer Vision Research Engineer at a renowned cancer research center in France, where he designs and evaluates advanced neural architectures for complex medical-image analysis problems. His expertise focuses on generative AI, representation learning, and deep neural networks for clinical imaging applications. He has authored multiple peer-reviewed scientific publications as a primary author and has extensive experience developing research-grade systems with PyTorch and PyTorch Lightning. His technical projects, open-source work, and complete portfolio are available at arksyd96.github.io.

Table of Contents - Mastering PyTorch and Lightning

Chapter 0 — Genesis, Architecture, and Environment Setup

0.1 The Origins: Why PyTorch? 2
0.2 The Lightning Evolution: Scaling Deep-Learning Workflows 2
0.3 Industrial Use Cases: What Will You Build? 3
0.4 The GPU Matrix: Truth About CUDA & cuDNN 3
0.5 Setting Up Your Development Environment 4

Chapter 1 — The Anatomy of Tensors

1. Internal Architecture & Memory Allocation 7
2. Advanced Dimension & Stride Manipulation 11
3. Gotchas & Reality Checks: Production Pitfalls 15
4. QA: Multiple Choice Questionnaire 18
Chapter 1 Answers & Explanations 19

Chapter 2 — Tensor Mathematics & Advanced Indexing

1. Tensor Mathematics & Reductions 20
2. The Magic of Broadcasting 22
3. Advanced Indexing & Slicing 23
4. Gotchas & Reality Checks: Production Pitfalls 25
5. QA: Multiple Choice Questionnaire 27
Chapter 2 Answers & Explanations 28

Chapter 3 — Autograd & the Computational Graph

1. The Dynamic Computation Graph 29
2. The Engine: backward() and requires_grad 31
3. Breaking the Graph: Memory & Performance Management 33
4. Gotchas & Reality Checks: Production Pitfalls 35
5. QA: Multiple Choice Questionnaire 37
Chapter 3 Answers & Explanations 38

Chapter 4 — Designing Custom Architectures with torch.nn

1. The nn.Module and nn.Parameter Lifecycle 39
2. Creating Custom Layers & Activation Functions 43
3. Chaining Modules: nn.Sequential and nn.ModuleList 44
4. Gotchas & Reality Checks: Production Pitfalls 46
5. QA: Multiple Choice Questionnaire 48
Chapter 4 Answers & Explanations 49

Chapter 5 — Production-Ready Data Pipelines

1. Crafting Custom Datasets 51
2. Orchestrating the Pipeline: The DataLoader 54
3. Production Bottlenecks & Best Practices 57
4. QA: Multiple Choice Questionnaire 59
Chapter 5 Answers & Explanations 60

Chapter 6 — Crafting the Native Training Loop

1. The Anatomy of a Single Training Step 61
2. The Evaluation Phase & State Management 64
3. Assembling the Complete Native Loop 66
4. Gotchas & Reality Checks: Production Pitfalls 66
5. QA: Multiple Choice Questionnaire 70
Chapter 6 Answers & Explanations 71

Chapter 7 — Hardware Acceleration & Model Persistence

1. Multi-Device Management with Device-Agnostic Code 72
2. Model Persistence: Saving & Loading 74
3. Gotchas & Reality Checks: Production Pitfalls 77
4. QA: Multiple Choice Questionnaire 79
Chapter 7 Answers & Explanations 80

Chapter 8 — Capstone Project 1: Native PyTorch in Action

1. Architectural Primer: Convolutions & Latent Spaces 81
2. Project A: End-to-End MNIST Classifier 84
3. Project B: CIFAR-100 Denoising Autoencoder 88
4. QA: Post-Mortem Project Analysis 93
Chapter 8 Answers & Explanations 94

Chapter 9 — From Native PyTorch to Lightning Module

1. Separating Model Logic from Execution 96
2. Anatomy of a LightningModule 97
3. Trainer & Granular Compilation 102
4. Refactoring Guidelines 104
5. QA: Multiple Choice Questionnaire 106
Chapter 9 Answers & Explanations 107

Chapter 10 — Streamlining Data with Lightning DataModule

1. The Lifecycle of a LightningDataModule 108
2. Implementing a CIFAR-10 DataModule 109
3. Reproducibility & Team Collaboration 113
4. Gotchas & Reality Checks: Production Pitfalls 115
5. QA: Multiple Choice Questionnaire 117
Chapter 10 Answers & Explanations 118

Chapter 11 — Mastering the Lightning Trainer

1. Automating Training and Evaluation 119
2. Hardware & Precision Configuration 120
3. Extending the Trainer with Callbacks 121
4. Trainer Configuration as Experiment Policy 124
5. QA: Multiple Choice Questionnaire 125
Chapter 11 Answers & Explanations 126

Chapter 12 — Production Callbacks & Advanced Logging

1. How Callbacks Extend the Trainer 127
2. Logging Metrics and Hyperparameters 129
3. Gotchas & Reality Checks: Production Pitfalls 132
4. QA: Monitoring & Callbacks 134
Chapter 12 Answers & Explanations 135

Chapter 13 — Advanced Scale: Mixed Precision & Multi-GPU

1. Automatic Mixed Precision 136
2. Distributed Data Parallel Architecture 139
3. Configuring Distributed Training in Lightning 141
4. Gotchas & Reality Checks: Production Pitfalls 142
5. QA: Advanced Scale & Distribution 145
Chapter 13 Answers & Explanations 146

Chapter 14 — Capstone Project 2: Generative AI

1. Project A: Refactoring the Denoising Autoencoder 147
2. Project B: The Variational Autoencoder 153
3. QA: Senior Generative AI & Lightning Mechanics 158
Chapter 14 Answers & Explanations 159

Conclusion

From Research to Production 160

Chapter 0 — Genesis, Architecture, and Environment Setup
- 0.1 The Origins: Why PyTorch?
- 0.2 The Lightning Evolution: Scaling Deep-Learning Workflows
- 0.3 Industrial Use Cases: What Will You Build?
- 0.4 The GPU Matrix: Truth About CUDA & cuDNN
- 0.5 Setting Up Your Development Environment
Chapter 1 — The Anatomy of Tensors
- 1. Internal Architecture & Memory Allocation
- 2. Advanced Dimension & Stride Manipulation
- 3. Gotchas & Reality Checks: Production Pitfalls
- 4. QA: Multiple Choice Questionnaire
- Chapter 1 Answers & Explanations
Chapter 2 — Tensor Mathematics & Advanced Indexing
- 1. Tensor Mathematics & Reductions
- 2. The Magic of Broadcasting
- 3. Advanced Indexing & Slicing
- 4. Gotchas & Reality Checks: Production Pitfalls
- 5. QA: Multiple Choice Questionnaire
- Chapter 2 Answers & Explanations
Chapter 3 — Autograd & the Computational Graph
- 1. The Dynamic Computation Graph
- 2. The Engine: backward() and requires_grad
- 3. Breaking the Graph: Memory & Performance Management
- 4. Gotchas & Reality Checks: Production Pitfalls
- 5. QA: Multiple Choice Questionnaire
- Chapter 3 Answers & Explanations
Chapter 4 — Designing Custom Architectures with torch.nn
- 1. The nn.Module and nn.Parameter Lifecycle
- 2. Creating Custom Layers & Activation Functions
- 3. Chaining Modules: nn.Sequential and nn.ModuleList
- 4. Gotchas & Reality Checks: Production Pitfalls
- 5. QA: Multiple Choice Questionnaire
- Chapter 4 Answers & Explanations
Chapter 5 — Production-Ready Data Pipelines
- 1. Crafting Custom Datasets
- 2. Orchestrating the Pipeline: The DataLoader
- 3. Production Bottlenecks & Best Practices
- 4. QA: Multiple Choice Questionnaire
- Chapter 5 Answers & Explanations
Chapter 6 — Crafting the Native Training Loop
- 1. The Anatomy of a Single Training Step
- 2. The Evaluation Phase & State Management
- 3. Assembling the Complete Native Loop
- 4. Gotchas & Reality Checks: Production Pitfalls
- 5. QA: Multiple Choice Questionnaire
- Chapter 6 Answers & Explanations
Chapter 7 — Hardware Acceleration & Model Persistence
- 1. Multi-Device Management with Device-Agnostic Code
- 2. Model Persistence: Saving & Loading
- 3. Gotchas & Reality Checks: Production Pitfalls
- 4. QA: Multiple Choice Questionnaire
- Chapter 7 Answers & Explanations
Chapter 8 — Capstone Project 1: Native PyTorch in Action
- 1. Architectural Primer: Convolutions & Latent Spaces
- 2. Project A: End-to-End MNIST Classifier
- 3. Project B: CIFAR-100 Denoising Autoencoder
- 4. QA: Post-Mortem Project Analysis
- Chapter 8 Answers & Explanations
Chapter 9 — From Native PyTorch to Lightning Module
- 1. Separating Model Logic from Execution
- 2. Anatomy of a LightningModule
- 3. Trainer & Granular Compilation
- 4. Refactoring Guidelines
- 5. QA: Multiple Choice Questionnaire
- Chapter 9 Answers & Explanations
Chapter 10 — Streamlining Data with Lightning DataModule
- 1. The Lifecycle of a LightningDataModule
- 2. Implementing a CIFAR-10 DataModule
- 3. Reproducibility & Team Collaboration
- 4. Gotchas & Reality Checks: Production Pitfalls
- 5. QA: Multiple Choice Questionnaire
- Chapter 10 Answers & Explanations
Chapter 11 — Mastering the Lightning Trainer
- 1. Automating Training and Evaluation
- 2. Hardware & Precision Configuration
- 3. Extending the Trainer with Callbacks
- 4. Trainer Configuration as Experiment Policy
- 5. QA: Multiple Choice Questionnaire
- Chapter 11 Answers & Explanations
Chapter 12 — Production Callbacks & Advanced Logging
- 1. How Callbacks Extend the Trainer
- 2. Logging Metrics and Hyperparameters
- 3. Gotchas & Reality Checks: Production Pitfalls
- 4. QA: Monitoring & Callbacks
- Chapter 12 Answers & Explanations
Chapter 13 — Advanced Scale: Mixed Precision & Multi-GPU
- 1. Automatic Mixed Precision
- 2. Distributed Data Parallel Architecture
- 3. Configuring Distributed Training in Lightning
- 4. Gotchas & Reality Checks: Production Pitfalls
- 5. QA: Advanced Scale & Distribution
- Chapter 13 Answers & Explanations
Chapter 14 — Capstone Project 2: Generative AI
- 1. Project A: Refactoring the Denoising Autoencoder
- 2. Project B: The Variational Autoencoder
- 3. QA: Senior Generative AI & Lightning Mechanics
- Chapter 14 Answers & Explanations
Conclusion: From Research to Production

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