PyTorch Pocket Reference: Building and Deploying Deep Learning Models

Preface
    Who Should Read This Book
    Why I Wrote This Book
    Navigating This Book
    Conventions Used in This Book
    Using Code Examples
    O’Reilly Online Learning
    How to Contact Us
    Acknowledgments
1. An Introduction to PyTorch
    What Is PyTorch?
    Why Use PyTorch?
    Getting Started
        Running in Google Colaboratory
        Running on a Local Computer
        Running on Cloud Platforms
            Running on AWS
            Running on Microsoft Azure
            Running on Google Cloud Platform
        Verifying Your PyTorch Environment
    A Fun Example
2. Tensors
    What Is a Tensor?
        Simple CPU Example
        Simple GPU Example
        Moving Tensors Between CPUs and GPUs
    Creating Tensors
        Tensor Attributes
        Data Types
        Creating Tensors from Random Samples
        Creating Tensors Like Other Tensors
    Tensor Operations
        Indexing, Slicing, Combining, and Splitting Tensors
        Tensor Operations for Mathematics
        Automatic Differentiation (Autograd)
3. Deep Learning Development  with PyTorch
    The Overall Process
    Data Preparation
        Data Loading
        Data Transforms
        Data Batching
        General Data Preparation (torch.utils.data)
            Dataset classes
            Sampler classes
            DataLoader classes
    Model Development
        Model Design
            Using existing and pretrained models
            The PyTorch NN module (torch.nn)
        Training
            Fundamental training loop
            Loss functions
            Optimizer algorithms
        Validation
        Testing
    Model Deployment
        Saving Models
        Deploying to PyTorch Hub
        Deploying to Production
4. Neural Network Development Reference Designs
    Image Classification with Transfer Learning
        Data Processing
        Model Design
        Training and Validation
        Testing and Deployment
    Sentiment Analysis with Torchtext
        Data Processing
        Model Design
        Training and Validation
        Testing and Deployment
    Generative Learning—Generating Fashion-MNIST Images with DCGAN
        Data Processing
        Model Design
        Training
        Testing and Deployment
5. Customizing PyTorch
    Custom Layers and Activations
        Custom Layer Example (Complex Linear)
        Custom Activation Example (Complex ReLU)
    Custom Model Architectures
    Custom Loss Functions
    Custom Optimizer Algorithms
    Custom Training, Validation, and Test Loops
6. PyTorch Acceleration and Optimization
    PyTorch on a TPU
    PyTorch on Multiple GPUs (Single Machine)
        Data Parallel Processing
            The multithreaded approach using nn.DataParallel
            The multiprocess approach using DDP (preferred)
        Model Parallel Processing
        Combined Data Parallel Processing and Model Parallel Processing
    Distributed Training (Multiple Machines)
    Model Optimization
        Hyperparameter Tuning
        Quantization
        Pruning
            Pruning model example
            Local and global pruning
            Pruning API
            Custom pruning methods
7. Deploying PyTorch to Production
    PyTorch Deployment Tools and Libraries
        Common Example Model
        Python API
        TorchScript
        TorchServe
            Install TorchServe and torch-model-archiver
            Package a model archive
            Run TorchServe
            Request predictions
            Logging and monitoring
        ONNX
        Mobile Libraries
    Deploying to a Flask App
    Colab Flask App
    Deploying to the Cloud with TorchServe
    Quick Start with Docker
    Deploying to Mobile and Edge
        iOS
        Android
        Other Edge Devices
8. The PyTorch Ecosystem and Additional Resources
    The PyTorch Ecosystem
    Torchvision for Image and Video
        Datasets and I/O
        Models
        Transforms, Operations, and Utilities
    Torchtext for NLP
        Create a Dataset Object
        Preprocess Data
        Create a Dataloader for Batching
        Data (torchtext.data)
        Datasets (torchtext.datasets)
        Vocabularies (torchtext.vocab)
    TensorBoard for Visualization
        Learning Curves with SCALARS
        Model Architectures with GRAPHS
        Data with IMAGES, TEXT, and PROJECTOR
        Weight Distributions with DISTRIBUTIONS  and HISTOGRAMS
        Hyperparameters with HPARAMS
        The TensorBoard API
    Papers with Code
    Additional PyTorch Resources
        Tutorials
        Books
        Online Courses and Live Training
Index