Practical Computer Vision Applications Using Deep Learning with CNNs : With Detailed Examples in Python Using TensorFlow and Kivy （1st）

Gad, Ahmed Fawzy

APress（2018/12発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 405 p.
• 言語 ENG
• 商品コード 9781484241660
• DDC分類 006

Full Description

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 ForData scientists, machine learning and deep learning engineers, software developers.

Contents

 1. Recognition in Computer Vision.- 2. Artificial Neural Network.- 3. Classification using ANN with Engineered Features.- 4. ANN Parameters Optimization.- 5. Convolutional Neural Networks.- 6. TensorFlow Recognition Application.- 7. Deploying Pre-Trained Models.- 8. Cross-Platform Data Science Applications.Appendix: Uploading Projects to PyPI.