Overview
- Explains the basic concepts of deep learning using numerical examples
- Discusses the practical use of deep convolutional neural networks in computer vision with Python
- Covers deploying trained models
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Table of contents (8 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Practical Computer Vision Applications Using Deep Learning with CNNs
Book Subtitle: With Detailed Examples in Python Using TensorFlow and Kivy
Authors: Ahmed Fawzy Gad
DOI: https://doi.org/10.1007/978-1-4842-4167-7
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Ahmed Fawzy Gad 2018
Softcover ISBN: 978-1-4842-4166-0Published: 06 December 2018
eBook ISBN: 978-1-4842-4167-7Published: 05 December 2018
Edition Number: 1
Number of Pages: XXII, 405
Number of Illustrations: 200 b/w illustrations
Topics: Artificial Intelligence, Python, Open Source