PyTorch Computer Vision Cookbook

Avendi, Michael

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商品描述

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.

作者簡介

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.

目錄大綱

  1. Getting Started with PyTorch for Deep Learning
  2. Binary Image Classification
  3. Multi-class Image Classification
  4. Single-object detection
  5. Multi-object detection
  6. Single-object Segmentation
  7. Multi-object Segmentation
  8. Neural Style Transfer with PyTorch
  9. GANs and Adversarial Examples
  10. Video Processing with PyTorch