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Modern Computer Vision with PyTorch
Modern Computer Vision with PyTorch

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

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Profile Icon V Kishore Ayyadevara Profile Icon Yeshwanth Reddy
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Mex$1082.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (20 Ratings)
eBook Nov 2020 824 pages 1st Edition
eBook
Mex$1082.99
Paperback
Mex$1353.99
Subscription
Free Trial
Arrow left icon
Profile Icon V Kishore Ayyadevara Profile Icon Yeshwanth Reddy
Arrow right icon
Mex$1082.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (20 Ratings)
eBook Nov 2020 824 pages 1st Edition
eBook
Mex$1082.99
Paperback
Mex$1353.99
Subscription
Free Trial
eBook
Mex$1082.99
Paperback
Mex$1353.99
Subscription
Free Trial

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Key benefits

  • 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

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.

Who is this book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Nov 27, 2020
Length: 824 pages
Edition : 1st
Language : English
ISBN-13 : 9781839216534
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Product Details

Publication date : Nov 27, 2020
Length: 824 pages
Edition : 1st
Language : English
ISBN-13 : 9781839216534
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Frequently bought together


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Total Mex$ 3,589.97
Modern Computer Vision with PyTorch
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Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7
(20 Ratings)
5 star 85%
4 star 5%
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Jolo Tolentino Mar 27, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book! You get a lot of tutorials on the internet but nothing beats a structured learning path .
Amazon Verified review Amazon
Parth Patel May 11, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The authors have done a great job in introducing CV and DL to tackle problems head-first. All the mechanics and techniques relevant to the current CV problems are available in the book. The book has easy to understand theory and implementable code along with each sub-section.I had to solve CV problem related to satellite imagery during my final semester internship (B.Tech). The documentation on the same was scarce and I ended up buying the kindle ebook. The only difficult part for me to understand was that which section in book was relevant to my problem. After figuring that out, the rest was relatively easy. Although the coding examples in the book were entirely different from my problem, the tools and techniques are the same for the most part.For someone finds a CV-DL problem on his/her hands, I highly recommend this book. I was able to solve my problem statement satisfactorily and graduated with great grades in the final internship semester. Most importantly, I learned a lot. The advanced tools and techniques that are available today will blow your mind.
Amazon Verified review Amazon
Billy Mar 11, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A massive book worthy of your time and attention.Truly a major piece of work that should not go unnoticed!
Amazon Verified review Amazon
AbhijitP May 31, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you are someone who wants to quickly get started to apply deep learning to computer vision , and are new to DL, this book will get you hands on with required theory and then coding for the same, in relatively short time.It covers range of topics from classification , object detection. Segmentation, GANs, etc.Beautifully illustrated, theories are explained with examples, you get to train your own code on datasets, hoe to improve model performance etc.Author has done a very good job, my sincere thanks to them, I got to learn a lot regarding Using Deep learning in Computer vision....Do buy this book, you won't regret it...The amount of knowledge it imparts is priceless....
Amazon Verified review Amazon
kranthi Jan 14, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
There are increasing resources with scattered information and use cases of computer vision. The book serves timely with all the required information in one place. No distractions
Amazon Verified review Amazon
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