Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide
- Length: 1187 pages
- Edition: 1
- Language: English
- Publisher: Leanpub
- Publication Date: 2021-05-18
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.
Preface
Acknowledgements
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
1. Anaconda
2. Conda (Virtual) Environments
3. PyTorch
Using GPU / CUDA
Using CPU
4. TensorBoard
5. GraphViz and Torchviz (optional)
6. Git
7. Jupyter
Moving On
Part I: Fundamentals
Chapter 0: Visualizing Gradient Descent
Spoilers
Jupyter Notebook
Imports
Visualizing Gradient Descent
Model
Data Generation
Synthetic Data Generation
Train-Validation-Test Split
Step 0 - Random Initialization
Step 1 - Compute Model’s Predictions
Step 2 - Compute the Loss
Loss Surface
Cross-Sections
Step 3 - Compute the Gradients
Visualizing Gradients
Backpropagation
Step 4 - Update the Parameters
Learning Rate
Small Learning Rate
Big Learning Rate
Very Big Learning Rate
"Bad" Feature
Scaling / Standardizing / Normalizing
Step 5 - Rinse and Repeat!
The Path of Gradient Descent
Recap
Chapter 1: A Simple Regression Problem
Spoilers
Jupyter Notebook
Imports
A Simple Regression Problem
Data Generation
Synthetic Data Generation
Gradient Descent
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!
Linear Regression in Numpy
PyTorch
Tensor
Loading Data, Devices and CUDA
Creating Parameters
Autograd
backward
grad
zero_
Updating Parameters
no_grad
Dynamic Computation Graph
Optimizer
step / zero_grad
Loss
Model
Parameters
state_dict
Device
Forward Pass
train
Nested Models
Sequential Models
Layers
Putting It All Together
Data Preparation
Model Configuration
Model Training
Recap
Chapter 2: Rethinking the Training Loop
Spoilers
Jupyter Notebook
Imports
Rethinking the Training Loop
Training Step
Dataset
TensorDataset
DataLoader
Mini-Batch Inner Loop
Random Split
Evaluation
Plotting Losses
TensorBoard
Running It Inside a Notebook
Running It Separately (Local Installation)
Running It Separately (Binder)
SummaryWriter
add_graph
add_scalars
Saving and Loading Models
Model State
Saving
Resuming Training
Deploying / Making Predictions
Setting the Model’s Mode
Putting It All Together
Recap
Chapter 2.1: Going Classy
Spoilers
Jupyter Notebook
Imports
Going Classy
The Class
The Constructor
Arguments
Placeholders
Variables
Functions
Training Methods
Saving and Loading Methods
Visualization Methods
The Full Code
Classy Pipeline
Model Training
Making Predictions
Checkpointing
Resuming Training
Putting It All Together
Recap
Chapter 3: A Simple Classification Problem
Spoilers
Jupyter Notebook
Imports
A Simple Classification Problem
Data Generation
Data Preparation
Model
Logits
Probabilities
Odds Ratio
Log Odds Ratio
From Logits to Probabilities
Sigmoid
Logistic Regression
Loss
BCELoss
BCEWithLogitsLoss
Imbalanced Dataset
Model Configuration
Model Training
Decision Boundary
Classification Threshold
Confusion Matrix
Metrics
True and False Positive Rates
Precision and Recall
Accuracy
Trade-offs and Curves
Low Threshold
High Threshold
ROC and PR Curves
The Precision Quirk
Best and Worst Curves
Comparing Models
Further Reading
Putting It All Together
Recap
Part II: Computer Vision
Chapter 4: Classifying Images
Spoilers
Jupyter Notebook
Imports
Classifying Images
Data Generation
NCHW vs NHWC
Torchvision
Datasets
Models
Transforms
Transforms on Images
Transforms on Tensor
Normalize Transform
Composing Transforms
Data Preparation
Dataset Transforms
SubsetRandomSampler
Data Augmentation Transforms
WeightedRandomSampler
Seeds and more (seeds)
Putting It Together
Pixels as Features
Shallow Model
Notation
Model Configuration
Model Training
Deep-ish Model
Model Configuration
Model Training
Show Me the Math!
Show Me the Code!
Weights as Pixels
Activation Functions
Sigmoid
Hyperbolic Tangent (TanH)
Rectified Linear Unit (ReLU)
Leaky ReLU
Parametric ReLU (PReLU)
Deep Model
Model Configuration
Model Training
Show Me the Math Again!
Putting It All Together
Recap
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
Recap
Chapter 5: Convolutions
Spoilers
Jupyter Notebook
Imports
Convolutions
Filter / Kernel
Convolving
Moving Around
Shape
Convolving in PyTorch
Striding
Padding
A REAL Filter
Pooling
Flattening
Dimensions
Typical Architecture
LeNet-5
A Multiclass Classification Problem
Data Generation
Data Preparation
Loss
Logits
Softmax
LogSoftmax
Negative Log-Likelihood Loss
Cross-Entropy Loss
Classification Losses Showdown!
Model Configuration
Model Training
Visualizing Filters and More!
Visualizing Filters
Hooks
Visualizing Feature Maps
Visualizing Classifier Layers
Accuracy
Loader Apply
Putting It All Together
Recap
Chapter 6: Rock, Paper, Scissors
Spoilers
Jupyter Notebook
Imports
Rock, Paper, Scissors…
Rock Paper Scissors Dataset
Data Preparation
ImageFolder
Standardization
The Real Datasets
Three-Channel Convolutions
Fancier Model
Dropout
Two-Dimensional Dropout
Model Configuration
Optimizer
Learning Rate
Model Training
Accuracy
Regularizing Effect
Visualizing Filters
Learning Rates
Finding LR
Adaptive Learning Rate
Moving Average (MA)
EWMA
EWMA Meets Gradients
Adam
Visualizing Adapted Gradients
Stochastic Gradient Descent (SGD)
Momentum
Nesterov
Flavors of SGD
Learning Rate Schedulers
Epoch Schedulers
Validation Loss Scheduler
Schedulers in StepByStep - Part I
Mini-Batch Schedulers
Schedulers in StepByStep - Part II
Scheduler Paths
Adaptive vs Cycling
Putting It All Together
Recap
Chapter 7: Transfer Learning
Spoilers
Jupyter Notebook
Imports
Transfer Learning
ImageNet
ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
ILSVRC-2012
AlexNet (SuperVision Team)
ILSVRC-2014
VGG
Inception (GoogLeNet Team)
ILSVRC-2015
ResNet (MSRA Team)
Comparing Architectures
Transfer Learning in Practice
Pre-Trained Model
Adaptive Pooling
Loading Weights
Model Freezing
Top of the 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
Recap
Extra Chapter: Vanishing and Exploding Gradients
Spoilers
Jupyter Notebook
Imports
Vanishing and Exploding Gradients
Vanishing Gradients
Ball Dataset and Block Model
Weights, Activations, and Gradients
Initialization Schemes
Batch Normalization
Exploding Gradients
Data Generation & Preparation
Model Configuration & Training
Gradient Clipping
Value Clipping
Norm Clipping (or Gradient Scaling)
Model Configuration & Training
Clipping with Hooks
Recap
Part III: Sequences
Chapter 8: Sequences
Spoilers
Jupyter Notebook
Imports
Sequences
Data Generation
Recurrent Neural Networks (RNNs)
RNN Cell
RNN Layer
Shapes
Stacked RNN
Bidirectional RNN
Square Model
Data Generation
Data Preparation
Model Configuration
Model Training
Visualizing the Model
Transformed Inputs
Hidden States
The Journey of a Hidden State
Can We Do Better?
Gated Recurrent Units (GRUs)
GRU Cell
GRU Layer
Square Model II - The Quickening
Model Configuration & Training
Visualizing the Model
Hidden States
The Journey of a Gated Hidden State
Can We Do Better?
Long Short-Term Memory (LSTM)
LSTM Cell
LSTM Layer
Square Model III - The Sorcerer
Model Configuration & Training
Visualizing the Hidden States
Variable-Length Sequences
Padding
Packing
Unpacking (to padded)
Packing (from padded)
Variable-Length Dataset
Data Preparation
Collate Function
Square Model IV - Packed
Model Configuration & Training
1D Convolutions
Shapes
Multiple Features or Channels
Dilation
Data Preparation
Model Configuration & Training
Visualizing the Model
Putting It All Together
Fixed-Length Dataset
Variable-Length Dataset
There Can Be Only ONE… Model
Model Configuration & Training
Recap
Chapter 9 - Part I: Sequence-To-Sequence
Spoilers
Jupyter Notebook
Imports
Sequence-To-Sequence
Data Generation
Encoder-Decoder Architecture
Encoder
Decoder
Teacher Forcing
Encoder + Decoder
Data Preparation
Model Configuration & Training
Visualizing Predictions
Can We Do Better?
Attention
"Values"
"Keys" and "Queries"
Computing the Context Vector
Scoring Method
Attention Scores
Scaled Dot Product
Attention Mechanism
Source Mask
Decoder
Encoder + Decoder + Attention
Model Configuration & Training
Visualizing Predictions
Visualizing Attention
Multi-Headed Attention
Chapter 9 - Part II: Sequence-To-Sequence
Spoilers
Self-Attention
Encoder
Cross-Attention
Decoder
Subsequent Inputs and Teacher Forcing
Attention Scores
Target Mask (Training)
Target Mask (Evaluation/Prediction)
Encoder + Decoder + Self-Attention
Model Configuration & Training
Visualizing Predictions
Sequential No More
Positional Encoding (PE)
Encoder + Decoder + PE
Model Configuration & Training
Visualizing Predictions
Visualizing Attention
Putting It All Together
Data Preparation
Model Assembly
Encoder + Decoder + Positional Encoding
Self-Attention "Layers"
Attention Heads
Model Configuration & Training
Recap
Chapter 10: Transform and Roll Out
Spoilers
Jupyter Notebook
Imports
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
Our Seq2Seq Problem
Projections or Embeddings
The Transformer
Data Preparation
Model Configuration & Training
Visualizing Predictions
The PyTorch Transformer
Model Configuration & Training
Visualizing Predictions
Vision Transformer
Data Generation & Preparation
Patches
Rearranging
Embeddings
Special Classifier Token
The Model
Model Configuration & Training
Putting It All Together
Data Preparation
Model Assembly
1. Encoder-Decoder
2. Encoder
3. Decoder
4. Positional Encoding
5. Encoder "Layer"
6. Decoder "Layer"
7. "Sub-Layer" Wrapper
8. Multi-Headed Attention
Model Configuration & Training
Recap
Part IV: Natural Language Processing
Chapter 11: Down the Yellow Brick Rabbit Hole
Spoilers
Jupyter Notebook
Additional Setup
Imports
"Down the Yellow Brick Rabbit Hole"
Building a Dataset
Sentence Tokenization
HuggingFace’s Dataset
Loading a Dataset
Attributes
Methods
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
What Is an Embedding Anyway?
Pretrained Word2Vec
Global Vectors (GloVe)
Using Word Embeddings
Vocabulary Coverage
Tokenizer
Special Tokens' Embeddings
Model I - GloVE + Classifier
Data Preparation
Pretrained PyTorch Embeddings
Model Configuration & Training
Model II - GloVe + Transformer
Visualizing Attention
Contextual Word Embeddings
ELMo
BERT
Document Embeddings
Model III - Preprocessed Embeddings
Data Preparation
Model Configuration & Training
BERT
Tokenization
Input Embeddings
Pretraining Tasks
Masked Language Model (MLM)
Next Sentence Prediction (NSP)
Outputs
Model IV - Classifying using BERT
Data Preparation
Model Configuration & Training
Fine-Tuning with HuggingFace
Sequence Classification (or Regression)
Tokenized Dataset
Trainer
Predictions
Pipelines
More Pipelines
GPT-2
Putting It All Together
Data Preparation
"Packed" Dataset
Model Configuration & Training
Generating Text
Recap
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