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PyTorch Artificial Intelligence Fundamentals: A recipe-based approach to design,build and deploy your own AI models with PyTorch 1.x
by 作者: Jibin Mathew
pages 页数: 200 pages
Publisher Finelybook 出版社: Packt Publishing (February 28,2020)
Language 语言: English
ISBN-10 书号: 1838557040
ISBN-13 书号: 9781838557041
Book Description
Use PyTorch to build end-to-end artificial intelligence systems using Python
Artificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains,but it is a complex and daunting topic. In this book,you’ll get to grips with building deep learning apps,and how you can use PyTorch for research and solving real-world problems.
This book uses a recipe-based approach,starting with the basics of tensor manipulation,before covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks,you’ll build a medical image classifier using deep learning. Next,you’ll use TensorBoard for visualizations. You’ll also delve into Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) before finally deploying your models to production at scale. You’ll discover solutions to common problems faced in machine learning,deep learning,and reinforcement learning. You’ll learn to implement AI tasks and tackle real-world problems in computer vision,natural language processing (NLP),and other real-world domains.
By the end of this book,you’ll have the foundations of the most important and widely used techniques in AI using the PyTorch framework.
What you will learn
Perform tensor manipulation using PyTorch
Train a fully connected neural network
Advance from simple neural networks to convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
Implement transfer learning techniques to classify medical images
Get to grips with generative adversarial networks (GANs),along with their implementation
Build deep reinforcement learning applications and learn how agents interact in the real environment
Scale models to production using ONNX Runtime
Deploy AI models and perform distributed training on large datasets
Contents
Preface
Chapter 1: Working with Tensors Using PyTorch
Chapter 2: Dealing with Neural Networks
Chapter 3: Convolutional Neural Networks for Computer Vision
Chapter 4: Recurrent Neural Networks for NLP
Chapter 5: Transfer Learning and TensorBoard
Chapter 6: Exploring Generative Adversarial Networks
Chapter 7: Deep Reinforcement Learning
Chapter 8: Productionizing Al Models in PyTorch
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Index
