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

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)

組織花費大量資源開發能夠像人一樣執行的軟體。圖像分類、物體檢測和追蹤、姿勢估計、臉部識別和情感估計在解決計算機視覺問題中扮演著重要角色。

本書將聚焦於這些以及其他深度學習架構和技術，幫助您使用Keras和TensorFlow庫創建解決方案。您還將回顧多個神經網絡架構，包括LeNet、AlexNet、VGG、Inception、R-CNN、Fast R-CNN、Faster R-CNN、Mask R-CNN、YOLO和SqueezeNet，並通過最佳實踐、技巧、快捷方式和陷阱，看到它們如何與Python代碼一起工作。所有代碼片段都將被細分並進行詳細討論，以便您可以在相應的環境中實施相同的原則。

《使用深度學習的計算機視覺》提供了一個全面而簡潔的指南，將深度學習和計算機視覺結合起來，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：
- 檢查深度學習代碼和概念，將指導原則應用於自己的項目中
- 分類和評估各種架構，以更好地了解在不同使用情境下的選擇
- 深入了解基本深度學習功能的內部運作方式

本書適合對軟體工程和數據科學領域有專業實踐經驗的從業人員。強烈建議具備Python的工作知識。學生和從事與計算機視覺和深度學習相關領域的高級學位的創新者也適用。

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是一位經驗豐富的資料科學專業人士，擁有跨地域和零售、電信、製造、醫療保健和公用事業領域的豐富經驗。他是一位實踐技術專家，並帶領過多個機器學習和人工智慧項目。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，擔任首席資料科學家的職位。

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