PyTorch Computer Vision Cookbook: Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x 1st Edition

Book Description
Computer vision techniques play an integral role in helping developers gain a high-level understanding of digital images and videos. With this book, you’ll learn how to solve the trickiest problems in computer vision (CV) using the power of deep learning algorithms, and leverage the latest features of PyTorch 1.x to perform a variety of CV tasks.

Starting with a quick overview of the PyTorch library and key deep learning concepts, the book then covers common and not-so-common challenges faced while performing image recognition, image segmentation, object detection, image generation, and other tasks. Next, you’ll understand how to implement these tasks using various deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and generative adversarial networks (GANs). Using a problem-solution approach, you’ll learn how to solve any issue you might face while fine-tuning the performance of a model or integrating it into your application. Later, you’ll get to grips with scaling your model to handle larger workloads, and implementing best practices for training models efficiently.

By the end of this CV book, you’ll be proficient in confidently solving many CV related problems using deep learning and PyTorch.

What you will learn
Develop, train and deploy deep learning algorithms using PyTorch 1.x
Understand how to fine-tune and change hyperparameters to train deep learning algorithms
Perform various CV tasks such as classification, detection, and segmentation
Implement a neural style transfer network based on CNNs and pre-trained models
Generate new images and implement adversarial attacks using GANs
Implement video classification models based on RNN, LSTM, and 3D-CNN
Discover best practices for training and deploying deep learning algorithms for CV applications
Who this book is for
Computer vision professionals, data scientists, deep learning engineers, and AI developers looking for quick solutions for various computer vision problems will find this book useful. Intermediate-level knowledge of computer vision concepts, along with Python programming experience is required.

Table of Contents
Getting Started with PyTorch for Deep Learning
Binary Image Classification
Multi-class Image Classification
Single-object detection
Multi-object detection
Single-object Segmentation
Multi-object Segmentation
Neural Style Transfer with PyTorch
GANs and Adversarial Examples
Video Processing with PyTorch