Deep Learning with PyTorch Quick Start Guide
Packt Publishing Limited (Verlag)
978-1-78953-409-2 (ISBN)
Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.
Key Features
Clear and concise explanations
Gives important insights into deep learning models
Practical demonstration of key concepts
Book DescriptionPyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.
This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders.
You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.
By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.
What you will learn
Set up the deep learning environment using the PyTorch library
Learn to build a deep learning model for image classification
Use a convolutional neural network for transfer learning
Understand to use PyTorch for natural language processing
Use a recurrent neural network to classify text
Understand how to optimize PyTorch in multiprocessor and distributed environments
Train, optimize, and deploy your neural networks for maximum accuracy and performance
Learn to deploy production-ready models
Who this book is forDevelopers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.
David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was a technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.
Table of Contents
Introduction to PyTorch
Deep Learning Fundamentals
Computational Graphs and Linear Models
Convolutional Networks
Other NN Architectures
Getting the Most out of PyTorch
| Erscheinungsdatum | 28.12.2018 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| ISBN-10 | 1-78953-409-7 / 1789534097 |
| ISBN-13 | 978-1-78953-409-2 / 9781789534092 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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