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

Price: $27.95
(as of Dec 15,2024 20:49:06 UTC – Details)

From the Publisher

Is this book for me?

Daniel wrote this book for beginners in general – not only PyTorch beginners. Every now and then he will spend some time explaining some fundamental concepts which are essential to have a proper understanding of what’s going on in the code.

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

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.

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.)
… and more!

How is this book different?

This book is written as if YOU, the reader, were having a conversation with Daniel, the author: he will ask you questions (and give you answers shortly afterward) and also make some (silly) jokes.

Moreover, this book spells concepts out in plain English, avoiding fancy mathematical notation as much as possible.

It shows you the inner workings of computer vision models, in a structured, incremental, and from-first-principles approach.

It builds, step-by-step, not only the models themselves but also your understanding as it shows you both the reasoning behind the code and how to avoid some common pitfalls and errors along the way.

“Hi, I’m Daniel!”

I am a data scientist, developer, teacher, and author of this series of books.

I will tell you, briefly, how this series of books came to be. In 2018, before teaching a class, I tried to find a blog post that would visually explain, in a clear and concise manner, the concepts behind binary cross-entropy so that I could show it to my students. Since I could not find any that fit my purpose, I decided to write one myself. It turned out to be my most popular blog post!

My readers have welcomed the simple, straightforward, and conversational way I explained the topic.

Then, in 2019, I used the same approach for writing another blog post: “Understanding PyTorch with an example: a step-by-step tutorial.” Once again, I was amazed by the reaction from the readers! It was their positive feedback that motivated me to write this series of books to help beginners start their journey into deep learning and PyTorch.

I hope you enjoy reading these books as much as I enjoyed writing them!

ASIN ‏ : ‎ B09QNZWW66
Publisher ‏ : ‎ Independently published (January 23, 2022)
Language ‏ : ‎ English
Paperback ‏ : ‎ 397 pages
ISBN-13 ‏ : ‎ 979-8482601273
Item Weight ‏ : ‎ 3.36 ounces
Dimensions ‏ : ‎ 7 x 0.9 x 10 inches

In this post, we will delve into the world of computer vision with PyTorch, a powerful deep learning framework. We will walk through the process of building and training a convolutional neural network (CNN) for image classification tasks.

Here are some of the topics we will cover in this beginner’s guide:

1. Introduction to computer vision and deep learning
2. Setting up PyTorch environment and loading data
3. Building a CNN architecture for image classification
4. Training and testing the model
5. Fine-tuning the model for better performance
6. Visualizing the learned features and predictions

By the end of this guide, you will have a solid understanding of how to use PyTorch for computer vision tasks and be able to build your own image classification models. Stay tuned for Volume II of our Deep Learning with PyTorch series!
#Deep #Learning #PyTorch #StepbyStep #Beginners #Guide #Volume #Computer #Vision