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

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 For Data scientists, machine learning and deep learning engineers, software developers. Front Matter ....Pages i-xxii Recognition in Computer Vision (Ahmed Fawzy Gad)....Pages 1-44 Artificial Neural Networks (Ahmed Fawzy Gad)....Pages 45-106 Recognition Using ANN with Engineered Features (Ahmed Fawzy Gad)....Pages 107-128 ANN Optimization (Ahmed Fawzy Gad)....Pages 129-181 Convolutional Neural Networks (Ahmed Fawzy Gad)....Pages 183-227 TensorFlow Recognition Application (Ahmed Fawzy Gad)....Pages 229-294 Deploying Pretrained Models (Ahmed Fawzy Gad)....Pages 295-338 Cross-Platform Data Science Applications (Ahmed Fawzy Gad)....Pages 339-380 Back Matter ....Pages 381-405