PyTorch Computer Vision Cookbook
暫譯: PyTorch 電腦視覺食譜

Avendi, Michael

PyTorch Computer Vision Cookbook
  • 出版商: Packt Publishing
  • 出版日期: 2020-03-20
  • 售價: $1,860
  • 貴賓價: 9.5 折 $1,767
  • 語言: 英文
  • 頁數: 364
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838644830
  • ISBN-13: 9781838644833
  • 相關分類: DeepLearning、Computer Vision

    • 海外代購書籍(需單獨結帳)

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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.

商品描述(中文翻譯)

電腦視覺技術在幫助開發者深入理解數位影像和影片方面扮演著不可或缺的角色。在這本書中，您將學習如何利用深度學習演算法的力量來解決電腦視覺（CV）中最棘手的問題，並利用 PyTorch 1.x 的最新功能來執行各種 CV 任務。

本書首先快速概述了 PyTorch 函式庫和關鍵的深度學習概念，接著涵蓋了在執行影像識別、影像分割、物件偵測、影像生成及其他任務時所面臨的常見及不常見挑戰。接下來，您將了解如何使用各種深度學習架構來實現這些任務，例如卷積神經網路（CNN）、遞迴神經網路（RNN）、長短期記憶（LSTM）和生成對抗網路（GAN）。透過問題解決的方法，您將學習如何解決在微調模型性能或將其整合到應用程式中時可能遇到的任何問題。之後，您將掌握如何擴展模型以處理更大的工作負載，以及實施有效訓練模型的最佳實踐。

在這本 CV 書的結尾，您將能夠自信地使用深度學習和 PyTorch 解決許多與 CV 相關的問題。

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.

作者簡介(中文翻譯)

邁克爾·阿文迪（Michael Avendi）是一位首席數據科學家，擁有豐富的深度學習、計算機視覺和醫學影像分析經驗。他專注於針對各種影像問題的數據驅動算法的研究與開發，包括醫學影像應用。他的研究論文已發表於多本主要的醫學期刊，包括《醫學影像分析》（Medical Imaging Analysis）期刊。邁克爾·阿文迪還是一位活躍的Kaggle參與者，並於2017年在Kaggle競賽中獲得了最高獎項。

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

目錄大綱(中文翻譯)


  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

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