PyTorch Recipes

A Problem-Solution Approach to Build, Train and Deploy Neural Network Models

Pradeepta Mishra

• Publisher: Apress
• Year: 2022
• ISBN:
  9781484289259
• Edition: 2
• Topic: Computer Science
• Subtopic: Programming in Python
• Index: Computer Science

Learn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code.
You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.
By the end of this book, you will be able to confidently build neural network models using PyTorch.
What You Will Learn

• Utilize new code snippets and models to train machine learning models using PyTorch
• Train deep learning models with fewer and smarter implementations
• Explore the PyTorch framework for model explainability and to bring transparency to model interpretation
• Build, train, and deploy neural network models designed to scale with PyTorch
• Understand best practices for evaluating and fine-tuning models using PyTorch
• Use advanced torch features in training deep neural networks
• Explore various neural network models using PyTorch
• Discover functions compatible with sci-kit learn compatible models
• Perform distributed PyTorch training and execution

Who This Book Is For Machine learning engineers, data scientists and Python programmers and software developers interested in learning the PyTorch framework.

Table of contents

• Cover
• Front Matter
• 1. Introduction to PyTorch, Tensors, and Tensor Operations
• 2. Probability Distributions Using PyTorch
• 3. CNN and RNN Using PyTorch
• 4. Introduction to Neural Networks Using PyTorch
• 5. Supervised Learning Using PyTorch
• 6. Fine-Tuning Deep Learning Models Using PyTorch
• 7. Natural Language Processing Using PyTorch
• 8. Distributed PyTorch Modelling, Model Optimization, and Deployment
• 9. Data Augmentation, Feature Engineering, and Extractions for Image and Audio
• 10. PyTorch Model Interpretability and Interface to Sklearn
• Back Matter