Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy - Softcover

Gad, Ahmed Fawzy

Softcover

ISBN 10: 1484241665 ISBN 13: 9781484241660

Publisher: Apress, 2018

Synopsis

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.

About this title

About the Author

Ahmed Fawzy Gad is a teaching assistant at the Faculty of Computers and Information (FCI), Menoufia University, Egypt. He has done his MSc in Computer Science. Ahmed is interested in deep learning, machine learning, computer vision, and Python. He aims to add value to the data science community by sharing his writings and tutorials. He is the author of the book "Practical Computer Vision Applications Using Deep Learning with CNN's" published by Apress. 

From the Back Cover

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 fully connected networks. 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 application and make the pre-trained models accessible over the Internet using Flask. 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. 

You will:

• 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
  

Edition details

• Publisher: Apress
• Publication date: 2018
• Language: English
• ISBN 10: 1484241665
• ISBN 13: 9781484241660
• Binding: Paperback
• Edition number: 1
• Number of pages: 427