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Spis treści

PyTorch Pocket Reference eBook -- spis treści

• Preface
  • Who Should Read This Book
  • Why I Wrote This Book
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly 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 LearningGenerating 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