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

ISBN-13: 9781484289242 / Angielski / Miękka / 2022 / 266 str.

Pradeepta Mishra

Opis

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.

Recenzje

"The book covers all important facets of neural network implementation and modeling, and could definitely be useful to students and developers keen for an in-depth look at how to build models using PyTorch, or how to engineer particular neural network features using this platform." (Mariana Damova, Computing Reviews, July 24, 2023)

Spis treści

Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations

Chapter Goal: This chapter is to understand what is PyTorch and its basic building blocks.

Chapter 2: Probability Distributions Using PyTorch

Chapter Goal: This chapter aims at covering different distributions compatible with PyTorch for data analysis.

 

Chapter 3: Neural Networks Using PyTorch

Chapter Goal: This chapter explains the use of PyTorch to develop a neural network model and optimize the model.

Chapter 4: Deep Learning (CNN and RNN) Using PyTorch

Chapter Goal: This chapter explains the use of PyTorch to train deep neural networks for complex datasets.

Chapter 5: Language Modeling Using PyTorch

Chapter Goal: In this chapter, we are going to use torch text for natural language processing, pre-processing, and feature engineering. 

 

Chapter 6: Supervised Learning Using PyTorch

Goal: This chapter explains how supervised learning algorithms implementation with PyTorch.

 

Chapter 7: Fine Tuning Deep Learning Models using PyTorch

Goal: This chapter explains how to Fine Tuning Deep Learning Models using the PyTorch framework.

Chapter 8: Distributed PyTorch Modeling

Chapter Goal: This chapter explains the use of parallel processing using the PyTorch framework.

Chapter 9: Model Optimization Using Quantization Methods

Chapter Goal: This chapter explains the use of quantization methods to optimize the PyTorch models and hyperparameter tuning with ray tune. 

Chapter 10: Deploying PyTorch Models in Production

Chapter Goal: In this chapter we are going to use torch serve, to deploy the PyTorch models into production.

 

Chapter 11: PyTorch for Audio

Chapter Goal: In this chapter torch audio will be used for audio resampling, data augmentation, features extractions, model training, and pipeline development.

 

Chapter 12: PyTorch for Image

Chapter Goal: This chapter aims at using Torchvision for image transformations, pre-processing, feature engineering, and model training.

 

Chapter 13: Model Explainability using Captum

Chapter Goal: In this chapter, we are going to use the captum library for model interpretability to explain the model as if you are explaining the model to a 5-year-old.

 

Chapter 14: Scikit Learn Model compatibility using Skorch

Chapter Goal: In this chapter, we are going to use skorch which is a high-level library for PyTorch that provides full sci-kit learn compatibility.

 

O autorze

Pradeepta Mishra is the Director of AI, Fosfor at L&T Infotech (LTI), leading a large group of Data Scientists, computational linguistics experts, Machine Learning and Deep Learning experts in building the next-generation product, ‘Leni,’ the world’s first virtual data scientist. He has expertise across core branches of Artificial Intelligence including Autonomous ML and Deep Learning pipelines, ML Ops, Image Processing, Audio Processing, Natural Language Processing (NLP), Natural Language Generation (NLG), design and implementation of expert systems, and personal digital assistants. In 2019 and 2020, he was named one of "India's Top "40Under40DataScientists" by Analytics India Magazine. Two of his books are translated into Chinese and Spanish based on popular demand. 

He delivered a keynote session at the Global Data Science conference 2018, USA. He has delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has mentored more than 2000 data scientists globally. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions, and community-arranged forums. He is a visiting faculty member to more than 10 universities, where he teaches deep learning and machine learning to professionals, and mentors them in pursuing a rewarding career in Artificial Intelligence.

Blurb

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.

You will:

• 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