About this book
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.
- 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
Deep Learning Computer Vision Python Machine Learning Neural Network Convolutional Neural Network Image Processing TensorFlow
- Book Title Practical Computer Vision Applications Using Deep Learning with CNNs
- Book Subtitle With Detailed Examples in Python Using TensorFlow and Kivy
- DOI https://doi.org/10.1007/978-1-4842-4167-7
- Copyright Information Ahmed Fawzy Gad 2018
- Publisher Name Apress, Berkeley, CA
- eBook Packages Professional and Applied Computing Professional and Applied Computing (R0)
- Softcover ISBN 978-1-4842-4166-0
- eBook ISBN 978-1-4842-4167-7
- Edition Number 1
- Number of Pages XXII, 405
- Number of Illustrations 200 b/w illustrations, 0 illustrations in colour
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