Deep Learning with PyTorch Step-by-Step
Deep Learning with PyTorch Step-by-Step
A Beginner's Guide
About the Book
If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it :-)
The book covers from the basics of gradient descent all the way up to fine-tuning large NLP models (BERT and GPT-2) using HuggingFace. It is divided into four parts:
- Part I: Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
- Part II: Computer Vision (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
- Part III: Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
- Part IV: Natural Language Processing (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time).
Moreover, this is not a formal book in any way: I am writing this book as if I were having a conversation with you, the reader. I will ask you questions (and give you answers shortly afterward) and I will also make (silly) jokes.
My job here is to make you understand the topic, so I will avoid fancy mathematical notation as much as possible and spell it out in plain English.
In this book, I will guide you through the development of many models in PyTorch, showing you why PyTorch makes it much easier and more intuitive to build models in Python: autograd, dynamic computation graph, model classes and much, much more.
We will build, step-by-step, not only the models themselves but also your understanding as I show you both the reasoning behind the code and how to avoid some common pitfalls and errors along the way.
I wrote this book for beginners in general - not only PyTorch beginners. Every now and then I will spend some time explaining some fundamental concepts which I believe are key to have a proper understanding of what's going on in the code.
Maybe you already know well some of those concepts: if this is the case, you can simply skip them, since I've made those explanations as independent as possible from the rest of the content.
About the Author
Table of Contents
- Preface
- About the Author
- Frequently Asked Questions (FAQ)
- Why PyTorch?
- Why this book?
- Who should read this book?
- What do I need to know?
- How to read this book?
- What’s Next?
- Setup Guide
- Official Repository
- Environment
- Google Colab
- Binder
- Local Installation
- Moving On
- Part I: Fundamentals
- Chapter 0: Visualizing Gradient Descent
- Visualizing Gradient Descent
- Model
- Data Generation
- Step 0: Random Initialization
- Step 1: Compute Model’s Predictions
- Step 2: Compute the Loss
- Step 3: Compute the Gradients
- Step 4: Update the Parameters
- Step 5: Rinse and Repeat!
- Chapter 1: A Simple Regression Problem
- A Simple Regression Problem
- Data Generation
- Gradient Descent
- Linear Regression in Numpy
- PyTorch
- Autograd
- Dynamic Computation Graph
- Optimizer
- Loss
- Model
- Chapter 2: Rethinking the Training Loop
- Rethinking the Training Loop
- Dataset
- DataLoader
- Evaluation
- TensorBoard
- Saving and Loading Models
- Chapter 2.1: Going Classy
- Going Classy
- The Class
- The Constructor
- Training Methods
- Saving and Loading Methods
- Visualization Methods
- The Full Code
- Classy Pipeline
- Model Training
- Making Predictions
- Checkpointing
- Resuming Training
- Going Classy
- Chapter 3: A Simple Classification Problem
- A Simple Classification Problem
- Data Generation
- Data Preparation
- Model
- Loss
- BCELoss
- BCEWithLogitsLoss
- Imbalanced Dataset
- Model Configuration
- Model Training
- Decision Boundary
- Classification Threshold
- Confusion Matrix
- Metrics
- Trade-offs and Curves
- Part II: Computer Vision
- Chapter 4: Classifying Images
- Classifying Images
- Torchvision
- Data Preparation
- Dataset Transforms
- SubsetRandomSampler
- Data Augmentation Transforms
- WeightedRandomSampler
- Seeds and more (seeds)
- Putting It Together
- Pixels as Features
- Shallow Model
- Deep-ish Model
- Activation Functions
- Deep Model
- Bonus Chapter: Feature Space
- Two-Dimensional Feature Space
- Transformations
- A Two-Dimensional Model
- Decision Boundary, Activation Style!
- More Functions, More Boundaries
- More Layers, More Boundaries
- More Dimensions, More Boundaries
- Chapter 5: Convolutions
- Spoilers
- Jupyter Notebook
- Convolutions
- Filter/Kernel
- Convolving
- Moving Around
- Shape
- Convolving in PyTorch
- Striding
- Padding
- A REAL Filter
- Pooling
- Flattening
- Dimensions
- Typical Architecture
- A Multiclass Classification Problem
- Data Generation
- Data Preparation
- Loss
- Classification Losses Showdown!
- Model Configuration
- Model Training
- Visualizing Filters and More!
- Static Method
- Visualizing Filters
- Hooks
- Visualizing Feature Maps
- Visualizing Classifier Layers
- Accuracy
- Loader Apply
- Putting It All Together
- Recap
- Chapter 6: Rock, Paper, Scissors
- Rock, Paper, Scissors...
- Data Preparation
- ImageFolder
- Standardization
- The Real Datasets
- Three-Channel Convolutions
- Fancier Model
- Dropout
- Model Configuration
- Model Training
- Learning Rates
- Finding LR
- Adaptive Learning Rate
- Stochastic Gradient Descent (SGD)
- Momentum
- Nesterov
- Flavors of SGD
- Learning Rate Schedulers
- Adaptive vs Cycling
- Chapter 7: Transfer Learning
- Transfer Learning
- ImageNet
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
- Transfer Learning in Practice
- Pre-Trained Model
- Model Configuration
- Data Preparation
- Model Training
- Generating a Dataset of Features
- Top Model
- Auxiliary Classifiers (Side-Heads)
- 1x1 Convolutions
- Inception Modules
- Batch Normalization
- Running Statistics
- Evaluation Phase
- Momentum
- BatchNorm2d
- Other Normalizations
- Small Summary
- Residual Connections
- Learning the Identity
- The Power of Shortcuts
- Residual Blocks
- Putting It All Together
- Fine-Tuning
- Feature Extraction
- Extra Chapter: Vanishing and Exploding Gradients
- Vanishing Gradients
- Initialization Schemes
- Batch Normalization
- Exploding Gradients
- Gradient Clipping
- Value Clipping
- Norm Clipping
- Clipping with Hooks
- Part III: Sequences
- Chapter 8: Sequences
- Sequences
- Data Generation
- Recurrent Neural Networks (RNNs)
- RNN Cell
- RNN Layer
- Shapes
- Stacked RNN
- Bidirectional RNN
- Square Model
- Visualizing the Model
- Transformed Inputs
- Hidden States
- The Journey of a Hidden State
- Gated Recurrent Units (GRUs)
- Long Short-Term Memory (LSTM)
- Variable-Length Sequences
- Padding
- Packing
- Unpacking (to padded)
- Packing (from padded)
- Variable-Length Dataset
- Collate Function
- 1D Convolutions
- Shapes
- Multiple Features or Channels
- Dilation
- Chapter 9: Sequence-to-Sequence
- Sequence-to-Sequence
- Encoder-Decoder Architecture
- Teacher Forcing
- Attention
- "Values"
- "Keys" and "Queries"
- Computing the Context Vector
- Scoring Method
- Attention Scores
- Scaled Dot Product
- Attention Mechanism
- Source Mask
- Decoder
- Encoder + Decoder + Attention
- Multi-Headed Attention
- Self-Attention
- Encoder
- Cross-Attention
- Decoder
- Subsequent Inputs and Teacher Forcing
- Target Mask
- Positional Encoding (PE)
- Chapter 10: Transform and Roll Out
- Transform and Roll Out
- Narrow Attention
- Chunking
- Multi-Headed Attention
- Stacking Encoders and Decoders
- Wrapping "Sub-Layers"
- Transformer Encoder
- Transformer Decoder
- Layer Normalization
- Batch vs Layer
- Projections or Embeddings
- The Transformer
- The PyTorch Transformer
- Vision Transformer
- Patches
- Special Classifier Token
- Part IV: Natural Language Processing
- Chapter 11: Down the Yellow Brick Rabbit Hole
- Down the Yellow Brick Rabbit Hole
- Building a Dataset
- Sentence Tokenization
- HuggingFace's Dataset
- Word Tokenization
- Vocabulary
- HuggingFace's Tokenizer
- Before Word Embeddings
- One-Hot Encoding (OHE)
- Bag-of-Words (BoW)
- Language Models
- N-grams
- Continuous Bag-of-Words (CBoW)
- Word Embeddings
- Word2Vec
- Global Vectors (GloVe)
- Using Word Embeddings
- Model I - GloVe + Classifier
- Model II - GloVe + Transformer
- Contextual Word Embeddings
- ELMo
- BERT
- Document Embeddings
- Model III - Preprocessed Embeddings
- BERT
- Tokenization
- Input Embeddings
- Pretraining Tasks
- Model IV - Classifying using BERT
- Fine-Tuning with HuggingFace
- Sequence Classification (or Regression)
- Trainer
- Pipelines
- GPT-2
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