Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy (Paperback)

Ahmed Fawzy Gad

Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy (Paperback)

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商品描述

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. 
 
For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.
 
After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.
 
This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. 
 
What You Will Learn 
  •  
  • Understand how ANNs and CNNs work 
  • Create computer vision applications and CNNs from scratch using Python
  • Follow a deep learning project from conception to production using TensorFlow
  • Use NumPy with Kivy to build cross-platform data science applications
 
Who This Book Is For
Data scientists, machine learning and deep learning engineers, software developers.
 

商品描述(中文翻譯)

部署深度學習應用程式到多個平台上。您將使用卷積神經網絡（CNN）深度學習模型和Python來處理計算機視覺應用程式。本書首先解釋傳統的機器學習流程，您將分析一個圖像數據集。在此過程中，您將涵蓋人工神經網絡（ANNs），並使用Python從頭開始構建一個ANN，然後使用遺傳算法進行優化。

為了自動化這個過程，本書強調了傳統手工製作特徵在計算機視覺中的局限性，以及為什麼CNN深度學習模型是最先進的解決方案。本書從頭開始討論CNN，以展示它們與完全連接的ANN（FCNN）的不同之處以及更高的效率。您將使用Python實現一個CNN，以便全面了解該模型。

在鞏固基礎知識後，您將使用TensorFlow構建一個實用的圖像識別模型，並使用Flask將其部署到Web服務器上，使其可以通過互聯網訪問。使用Kivy和NumPy，您將創建低開銷的跨平台數據科學應用程式。

本書將幫助您從頭開始逐步應用深度學習和計算機視覺概念，從構思到生產的全過程。

您將學到什麼：
- 理解ANN和CNN的工作原理
- 使用Python從頭開始創建計算機視覺應用程式和CNN
- 使用TensorFlow從構思到生產的深度學習項目
- 使用NumPy和Kivy構建跨平台數據科學應用程式

適合對象：
數據科學家、機器學習和深度學習工程師、軟件開發人員。

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