Avendi Michael. PyTorch Computer Vision Cookbook: Over 70 recipes to solve computer vision and image processing problems using PyTorch 1.x [Code Files]

Packt Publishing, 2020. — 355 p. — ISBN: 978-1-83864-483-3.Code files only!
Discover powerful ways to explore deep learning algorithms and solve real-world computer vision problems using Python
Developers can gain a high-level understanding of digital images and videos using computer vision techniques. With this book, you’ll learn how to solve the trickiest of 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 computer vision tasks.Starting with a quick overview of the PyTorch library and key deep learning concepts, the book covers common and not-so-common challenges faced while performing image recognition, image segmentation, captioning, image generation, and many other tasks. You’ll 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 solve any issue you might face while fine-tuning the performance of the model or integrating the model into your application. Additionally, you’ll even get to grips with scaling the model to handle larger workloads and implement best practices for training models efficiently.By the end of this book, you’ll be able to solve any problem relating to training effective computer vision models.What you will learn
Implement a multi-class image classification network using PyTorch
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 CNN and pre-trained models
Generate new images using generative adversarial networks
Implement video classification models based on RNN and LSTM
Discover best practices for training and deploying deep learning algorithms for CV applications