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PyTorch Computer Vision Cookbook: Over 70 recipes to master the art of computer vision with deep learning and PyTorch 1.x - Softcover
Synopsis
Discover powerful ways to use deep learning algorithms and solve real-world computer vision problems using Python
Key Features
- Solve the trickiest of problems in computer vision by combining the power of deep learning and neural networks
- Leverage PyTorch 1.x capabilities to perform image classification, object detection, and more
- Train and deploy enterprise-grade, deep learning models for computer vision applications
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
"synopsis" may belong to another edition of this title.
About the Author
Michael Avendi is a principal data scientist with vast experience in deep learning, computer vision, and medical imaging analysis. He works on the research and development of data-driven algorithms for various imaging problems, including medical imaging applications. His research papers have been published in major medical journals, including the Medical Imaging Analysis journal. Michael Avendi is an active Kaggle participant and was awarded a top prize in a Kaggle competition in 2017.
"About this title" may belong to another edition of this title.
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