Building Computer Vision Applications Using Artificial Neural Networks: With Step-By-Step Examples in Opencv and Tensorflow with Python (使用人工神經網絡構建電腦視覺應用:搭配 Python 的 OpenCV 和 TensorFlow 的逐步範例)

Ansari, Shamshad

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商品描述

Apply computer vision and machine learning concepts in developing business and industrial applications ​using a practical, step-by-step approach.

The book comprises four main sections starting with setting up your programming environment and configuring your computer with all the prerequisites to run the code examples. Section 1 covers the basics of image and video processing with code examples of how to manipulate and extract useful information from the images. You will mainly use OpenCV with Python to work with examples in this section.

Section 2 describes machine learning and neural network concepts as applied to computer vision. You will learn different algorithms of the neural network, such as convolutional neural network (CNN), region-based convolutional neural network (R-CNN), and YOLO. In this section, you will also learn how to train, tune, and manage neural networks for computer vision. Section 3 provides step-by-step examples of developing business and industrial applications, such as facial recognition in video surveillance and surface defect detection in manufacturing.

The final section is about training neural networks involving a large number of images on cloud infrastructure, such as Amazon AWS, Google Cloud Platform, and Microsoft Azure. It walks you through the process of training distributed neural networks for computer vision on GPU-based cloud infrastructure. By the time you finish reading Building Computer Vision Applications Using Artificial Neural Networks and working through the code examples, you will have developed some real-world use cases of computer vision with deep learning.

What You Will Learn

- Employ image processing, manipulation, and feature extraction techniques

- Work with various deep learning algorithms for computer vision

 

- Train, manage, and tune hyperparameters of CNNs and object detection models, such as R-CNN, SSD, and YOLO

- Build neural network models using Keras and TensorFlow

- Discover best practices when implementing computer vision applications in business and industry

- Train distributed models on GPU-based cloud infrastructure

 

Who This Book Is For

Data scientists, analysts, and machine learning and software engineering professionals with Python programming knowledge.

 

商品描述(中文翻譯)

應用計算機視覺和機器學習概念,以實用的逐步方法開發商業和工業應用程式。

本書分為四個主要部分,首先是設置編程環境並配置計算機以滿足運行代碼示例的所有先決條件。第一部分介紹了圖像和視頻處理的基礎知識,並提供了如何操作和提取圖像中有用信息的代碼示例。在本部分,您將主要使用Python和OpenCV來處理示例。

第二部分介紹了應用於計算機視覺的機器學習和神經網絡概念。您將學習不同的神經網絡算法,例如卷積神經網絡(CNN)、基於區域的卷積神經網絡(R-CNN)和YOLO。在本部分,您還將學習如何訓練、調整和管理計算機視覺的神經網絡。第三部分提供了開發商業和工業應用的逐步示例,例如視頻監控中的人臉識別和製造中的表面缺陷檢測。

最後一部分介紹了在雲基礎架構上訓練涉及大量圖像的神經網絡,例如Amazon AWS、Google Cloud Platform和Microsoft Azure。它將引導您完成在基於GPU的雲基礎架構上訓練計算機視覺的分佈式神經網絡的過程。通過閱讀《使用人工神經網絡構建計算機視覺應用》並完成代碼示例的操作,您將開發出一些具有深度學習的計算機視覺的實際應用案例。

您將學到什麼:

- 使用圖像處理、操作和特徵提取技術
- 使用各種深度學習算法進行計算機視覺
- 訓練、管理和調整CNN和物體檢測模型(如R-CNN、SSD和YOLO)的超參數
- 使用Keras和TensorFlow構建神經網絡模型
- 在商業和工業中實施計算機視覺應用的最佳實踐
- 在基於GPU的雲基礎架構上訓練分佈式模型

本書適合對Python編程有一定了解的數據科學家、分析師和機器學習及軟件工程專業人士。

作者簡介

Shamshad (Sam) Ansari works as President and CEO of Accure Inc, an artificial intelligence automation company that he founded. He has raised Accure from startup to a sustainable business by building a winning team and acquiring customers from across the globe. He has technical expertise in the area of computer vision, machine learning, AI, cognitive science, NLP, and big data. He architected, designed, and developed the Momentum platform that automates AI solution development. He is an inventor and has four US patents in the area of AI and cognitive computing.

 

Shamshad worked as a senior software engineer with IBM, VP of engineering with Orbit Solutions, and as principal architect and director of engineering with Apixio.

 

作者簡介(中文翻譯)

Shamshad (Sam) Ansari擔任Accure Inc的總裁兼首席執行官,該公司是他創立的一家人工智能自動化公司。他通過建立一支優勝的團隊並吸引全球客戶,將Accure從初創公司發展成一家可持續經營的企業。他在計算機視覺、機器學習、人工智能、認知科學、自然語言處理和大數據領域具有技術專長。他設計、開發了Momentum平台,該平台可以自動化人工智能解決方案的開發。他是一位發明家,在人工智能和認知計算領域擁有四項美國專利。

Shamshad曾在IBM擔任高級軟件工程師,Orbit Solutions擔任工程副總裁,以及Apixio擔任首席架構師和工程總監。