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“Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications (English Edition)”，作者：[V Kishore Ayyadevara, Yeshwanth Reddy]

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications (English Edition) Kindle电子书

V Kishore Ayyadevara (作者), Yeshwanth Reddy (作者) 格式: Kindle电子书

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Get to grips with deep learning techniques for building image processing applications using PyTorch with the help of code notebooks and test questions

Key Features

Implement solutions to 50 real-world computer vision applications using PyTorch
Understand the theory and working mechanisms of neural network architectures and their implementation
Discover best practices using a custom library created especially for this book

Book Description

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets.

You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud.

By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.

What you will learn

Train a NN from scratch with NumPy and PyTorch
Implement 2D and 3D multi-object detection and segmentation
Generate digits and DeepFakes with autoencoders and advanced GANs
Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN
Combine CV with NLP to perform OCR, image captioning, and object detection
Combine CV with reinforcement learning to build agents that play pong and self-drive a car
Deploy a deep learning model on the AWS server using FastAPI and Docker
Implement over 35 NN architectures and common OpenCV utilities

Who this book is for

This book is for beginners to PyTorch and intermediate-level machine learning practitioners who are looking to get well-versed with computer vision techniques using deep learning and PyTorch. If you are just getting started with neural networks, you’ll find the use cases accompanied by notebooks in GitHub present in this book useful. Basic knowledge of the Python programming language and machine learning is all you need to get started with this book.

Artificial Neural Network Fundamentals
PyTorch Fundamentals
Building a Deep Neural Network with PyTorch
Introducing Convolutional Neural Networks
Transfer Learning for object Classification
Practical Aspects of Image Classification
Basics of Object detection
Advanced object detection
Image segmentation
Applications of object detection and localization
Autoencoders and Image Manipulation
Image generation using GAN
Advanced GANs to manipulate images
Training with minimal data points
Combining Computer Vision and NLP techniques
Combining Computer Vision and Reinforcement Learning
Moving a Model to Production
OpenCV utilities for image analysis

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基本信息

ASIN : B08KG9X776
出版社 : Packt Publishing; 第 1st 版 (2020年11月27日)
出版日期 : 2020年11月27日
语言 : 英语
文件大小 : 65633 KB
标准语音朗读 : 已启用
X-Ray : 未启用
生词提示功能 : 未启用
纸书页数 : 824页

亚马逊热销商品排名: 商品里排第63,875名Kindle商店 (查看商品销售排行榜Kindle商店)
- 商品里排第31名Data & Databases（数据及数据库）
- 商品里排第4,218名英语学习与阅读

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美国亚马逊： 7 条评论

Sahib

Must have for anyone starting out, or looking to up their game using PyTorch

2021年2月28日 - 已在美国亚马逊上发表

I was invited by the publisher to review this book and provide my thoughts on that. Overall, I found the book to be very informative and well laid out. It takes you through the very basics of backpropagation to advanced implementations in PyTorch. The book assumes that the reader does have an understanding of Python at the very least, which I think beginners in deep learning should be aware of. Please find my honest thoughts on the book as follows:

What I liked:
I felt the book is very well structured and compiled. Unless you're looking for something very very specific, you'd be able to find techniques/implementations for any and all types of problems you are working on. They cover algorithms and implementations of basic neural networks, all the way upto RNNs and reinforcement learning with PyTorch. The breadth covered by this book on the number of techniques and algorithms is really amazing. Not only that, each of these topics are covered with amazing depth as well, making the book a must have for someone just starting out with PyTorch. The progression in difficulty of the book, also makes it beginner friendly.

What I would like to be there:
I would love to see 2 things added to this book:
1. Tensorboard setup with PyTorch, and usage. Although it's trivial, it would take some getting used to in setting up tensorboard with PyTorch. It's fairly common to use a graphical visualization tool like this while training and tracking your models. A small chapter, or a section in the appendix on how to setup tensorboard with PyTorch, and some examples to integrate that into your pipeline, would really be amazingly beneficial for any beginner/ intermediate user!

2. PyTorch models can't generally be used directly in the industry because most embedded chips don't have compilers for .pth models. Most chip manufacturers (unless you're using nvidia GPUs itself), have compilers implemented for either tensorflow, caffe models or the cross compatible ONNX format. I think a small section on converting your models into ONNX would really be beneficial for anyone just starting out and targeting embedded/high performance applications on the edge

All in all, I loved and enjoyed reading the book. It is a definite recommendation from my side for anyone who is starting out with PyTorch, or someone looking to implement advanced algorithms (more than just the basic feedforward networks or CNNs). Amazing work by Ayyadevara and Reddy! :)

1 个人发现此评论有用

Amazon Customer

Well-laid out text addressing the conceptual/practical foundations of computer vision with PyTorch

2021年1月21日 - 已在美国亚马逊上发表

Ayyadevara and Reddy’s 'Modern Computer Vision with PyTorch' is a well constructed beginner to intermediate level text on working more efficiently and creatively with PyTorch in image analysis and CV techniques. The chapters are well laid out in a logical progression from neural network and PyTorch fundamentals to more advanced topics in neural network design and model implementation. In addition to the theory behind the concepts, the authors include numerous and easy to follow examples, and make all code available via Github. This practical, hands-on approach allows readers to more easily grasp complex ideas and techniques. Supported by the innovative industry experience of both authors, the inclusion of a chapter on moving a model to production, a topic often overlooked, will be highly useful to the budding data scientist. This text acts as an excellent resource for those interested in both skill-building and -acquisition.

1 个人发现此评论有用

Xudong@SF

A very well written book

2021年2月9日 - 已在美国亚马逊上发表

I found this book is very well written. The book is a self-contained starting with the basics, the fundamentals about artificial neural networks, fundamentals about PyTorch and several basic deep learning examples using PyTorch.

The book then discusses important topics in computer vision, such as object classification and detection, image manipulations etc. The book also covers advanced topics, such as GANs, reinforcement learning, self-attention, few-shot learning etc. It is very handy that all these discussions come with concrete PyTorch examples.

It is a very good book for both beginners and the practitioners who want to learn the most recent updates and advances.

1 个人发现此评论有用

Reeti Pandey

A Guide to the latest and greatest techniques in Computer Vision

2021年1月11日 - 已在美国亚马逊上发表

This book is a comprehensive guide to the most advanced computer vision techniques, both from a conceptual and coding standpoint. Academic learners can leverage the detailed conceptual progression while it works as a Computer vision recipe for professionals. The book also covers the application aspects of computer vision in an industry setup. In terms of advanced and lesser-known computer vision techniques, the book works as a cheat sheet to implement computer vision in combination with NLP and reinforcement learning in PyTorch.

1 个人发现此评论有用

Steve

Glad to write the first review for this book

2020年12月31日 - 已在美国亚马逊上发表

I have been invited to write a review for this book, I have generally reviewed, I found this is a very good book if you are a beginner or interested in the deep learning field with using python. Most of this book introduced experiments or code snippets based on Pytorch which is a very popular deep learning library in academic research and industries. This book can equip you with an overview of the necessary knowledge along with practical implementations, after reading this book, it expects that at least you could solve basic problems or even harder ones in your tasks and you would feel be comfortable acquiring new knowledge in a fast way.

Enjoy reading!

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