Hands-On Java Deep Learning for Computer Vision - Implement machine learning and neural network methodologies to perform computer vision-related tasks.

Description

Although machine learning is an exciting world to explore, you may feel confused by all of its theoretical aspects. As a Java developer, you will be used to telling the computer exactly what to do, instead of being shown how data is generated; this causes many developers to struggle to adapt to machine learning. The goal of this book is to walk you through the process of efficiently training machine learning and deep learning models for Computer Vision using the most up-to-date techniques. The course is designed to familiarize you with neural networks, enabling you to train them efficiently, customize existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. You will build real-world Computer Vision applications, ranging from a simple Java handwritten digit recognition model to real-time Java autonomous car driving systems and face recognition models. By the end of this book, you will have mastered the best practices and modern techniques needed to build advanced Computer Vision Java applications and achieve production-grade accuracy. What You Will Learn ------------------- Discover neural Networks and their applications in Computer Vision Explore the popular Java frameworks and libraries for deep learning Build deep neural networks in Java Implement an end-to-end image classification application in Java Perform real-time video object detection using deep learning Enhance performance and deploy applications for production Cover......Page 1 Title Page......Page 2 Copyright and Credits......Page 3 About Packt......Page 4 Contributor......Page 5 Table of Contents......Page 6 Preface......Page 9 Chapter 1: Introduction to Computer Vision and Training Neural Networks......Page 14 The importance of data in deep learning algorithms......Page 15 Exploring neural networks......Page 16 Building a single neuron with multiple outputs......Page 17 Building a neural network......Page 19 How does a neural network learn? ......Page 22 Learning neural network weights......Page 26 Updating the neural network weights......Page 32 Advantages of deep learning......Page 35 Organizing your data......Page 37 Bias and variance......Page 39 Computational model efficiency......Page 41 Effective training techniques......Page 44 Initializing the weights......Page 50 Activation functions......Page 51 Optimizing algorithms......Page 53 Configuring the training parameters of the neural network......Page 60 Representing images and outputs......Page 65 Multiclass classification......Page 68 Building a handwritten digit recognizer......Page 72 Testing the performance of the neural network......Page 74 Summary......Page 76 Chapter 2: Convolutional Neural Network Architectures......Page 77 What is edge detection?......Page 78 Vertical edge detection......Page 79 Horizontal edge detection......Page 85 Edge detection intuition......Page 86 Types of filters......Page 88 Basic coding......Page 90 Convolution on RGB images......Page 100 Working with convolutional layers' parameters......Page 106 Padding......Page 108 Stride......Page 109 Max pooling......Page 111 Average pooling......Page 113 Pooling on RGB images......Page 114 Building and training a Convolution Neural Network......Page 115 Why convolution?......Page 117 Improving the handwritten digit recognition application......Page 119 Summary......Page 126 Working with classical networks......Page 127 AlexNet......Page 128 VGG-16......Page 130 Deep network performance......Page 132 ResNet-50......Page 135 The power of 1 x 1 convolutions and the inception network......Page 136 Applying transfer learning......Page 142 Neural networks......Page 143 Building an animal image classification – using transfer learning and VGG-16 architecture......Page 146 Summary......Page 153 Resolving object localization......Page 154 Labeling and defining data for localization......Page 155 Object localization prediction layer......Page 157 Landmark detection......Page 159 Object detection with the sliding window solution......Page 161 Disadvantages of sliding windows......Page 164 Convolutional sliding window ......Page 166 Detecting objects with the YOLO algorithm......Page 171 Max suppression......Page 174 Anchor boxes......Page 177 Building a real-time video, car, and pedestrian detection application......Page 179 Architecture of the application......Page 180 YOLO V2-optimized architecture......Page 181 Coding the application......Page 182 Summary......Page 189 What are convolution network layers learning?......Page 190 Neural style transfer......Page 197 Minimizing the cost function......Page 199 Applying content cost function......Page 201 Applying style cost function......Page 204 How to capture the style......Page 205 Style cost function......Page 207 Building a neural network that produces art......Page 210 Summary......Page 222 Chapter 6: Face Recognition......Page 223 Face verification......Page 224 Face recognition......Page 225 One-shot learning problem......Page 227 Similarity function......Page 228 Differentiating inputs with Siamese networks......Page 229 Learning with Siamese networks......Page 233 Exploring triplet loss......Page 234 Choosing the triplets......Page 237 Binary classification......Page 238 Building a face recognition Java application......Page 240 Summary......Page 247 Other Books You May Enjoy......Page 248 Index......Page 251