Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume II: Computer Vision

Voigt Godoy, Daniel

ISBN 13: 9798482601273

Published by Independently published, 2022

About this Item

Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Seller Inventory # M0B09QNZWW66-V

Bibliographic Details

• Title: Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume II: Computer Vision
• Publisher: Independently published
• Publication Date: 2022
• Language: English
• ISBN 13: 9798482601273
• Binding: Soft cover
• Condition: very good

About this title

Synopsis

Revised for PyTorch 2.x!

Why this book?

Are you looking for a book where you can learn about deep learning and PyTorch without having to spend hours deciphering cryptic text and code? A technical book that’s also easy and enjoyable to read?

This is it!

How is this book different?

• First, this book presents an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch.
• Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author.
• His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English.

What will I learn?

In this second volume of the series, you’ll be introduced to deeper models and activation functions, convolutional neural networks, initialization schemes, learning rate schedulers, transfer learning, and more.

By the time you finish this book, you’ll have a thorough understanding of the concepts and tools necessary to start developing, training, and fine-tuning computer-vision models using PyTorch.

If your goal is to learn about deep learning models for computer vision, and you’re already comfortable training simple models in PyTorch, the second volume is the right one for you.

What’s Inside

• Deep models, activation functions, and feature spaces
• Torchvision, datasets, models, and transforms
• Convolutional neural networks, dropout, and learning rate schedulers
• Transfer learning and fine-tuning popular models (ResNet, Inception, etc.)