Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras
暫譯: 使用深度學習的電腦視覺：基於Python和Keras的神經網絡架構

Verdhan, Vaibhav

Computer Vision Using Deep Learning: Neural Network Architectures with Python and Keras

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商品描述

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems.

This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments.

Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs.

What You'll Learn

  • Examine deep learning code and concepts to apply guiding principals to your own projects
  • Classify and evaluate various architectures to better understand your options in various use cases
  • Go behind the scenes of basic deep learning functions to find out how they work

Who This Book Is For

Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.

商品描述(中文翻譯)

組織在開發能夠模擬人類行為的軟體上投入了大量資源。影像分類、物件偵測與追蹤、姿勢估計、臉部辨識和情感估計在解決電腦視覺問題中扮演著重要角色。

本書將聚焦於這些及其他深度學習架構和技術，幫助您使用 Keras 和 TensorFlow 函式庫創建解決方案。您還將回顧多種神經網路架構，包括 LeNet、AlexNet、VGG、Inception、R-CNN、Fast R-CNN、Faster R-CNN、Mask R-CNN、YOLO 和 SqueezeNet，並了解它們如何與 Python 代碼一起運作，透過最佳實踐、技巧、捷徑和陷阱進行說明。所有代碼片段將被詳細拆解和討論，以便您能在各自的環境中實施相同的原則。

使用深度學習的電腦視覺提供了一個全面而簡明的指南，將深度學習 (DL) 和電腦視覺 (CV) 結合在一起，以自動化操作、減少人為干預、提高能力並降低成本。

您將學到什麼


  • 檢視深度學習代碼和概念，將指導原則應用於自己的專案

  • 分類和評估各種架構，以更好地了解在不同使用案例中的選擇

  • 深入了解基本深度學習功能的運作原理

本書適合誰閱讀

在軟體工程和數據科學領域工作的專業從業人員。強烈建議具備 Python 的工作知識。正在攻讀與電腦視覺和深度學習相關的高級學位的學生和創新者。

作者簡介

Vaibhav Verdhan is a seasoned data science professional with rich experience spanning across geographies and retail, telecom, manufacturing, health-care and utilities domain. He is a hands-on technical expert and has led multiple engagements in Machine Learning and Artificial Intelligence. He is a leading industry expert, is a regular speaker at conferences and meet-ups and mentors students and professionals. Currently he resides in Ireland and is working as a Principal Data Scientist.

作者簡介(中文翻譯)

Vaibhav Verdhan 是一位經驗豐富的數據科學專業人士，擁有跨地區及零售、電信、製造、醫療保健和公用事業領域的豐富經驗。他是一位實務技術專家，曾主導多個機器學習（Machine Learning）和人工智慧（Artificial Intelligence）相關的專案。他是業界的領先專家，經常在會議和聚會上發表演講，並指導學生和專業人士。目前，他居住在愛爾蘭，擔任首席數據科學家（Principal Data Scientist）。

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