Hands-On Java Deep Learning for Computer Vision Implement Machine Learning and Neural Network Methodologies to Perform Computer Vision-Related Tasks
This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world applications ranging from simple Java handwritten digit recognition models to real-time autonomous car driving systems and face recognition mo...
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| Médium: | E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Birmingham
Packt Publishing, Limited
2019
Packt Publishing Limited Packt Publishing |
| Vydání: | 1 |
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| ISBN: | 1789613965, 9781789613964 |
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| Abstract | This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world applications ranging from simple Java handwritten digit recognition models to real-time autonomous car driving systems and face recognition models using the popular Java-based libraries. |
|---|---|
| AbstractList | 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 familiarise you with neural networks, enabling you to train them efficiently, customise existing state-of-the-art architectures, build real-world Java applications, and get great results in a short space of time. This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world applications ranging from simple Java handwritten digit recognition models to real-time autonomous car driving systems and face recognition models using the popular Java-based libraries. |
| Author | Ramo, Klevis |
| Author_xml | – sequence: 1 fullname: Ramo, Klevis |
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| Snippet | This book will take you through the process of efficiently training deep neural networks in Java for Computer Vision-related tasks. You will build real-world... 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... |
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| SubjectTerms | Application program interfaces (Computer software) COM004000 COMPUTERS / Intelligence (AI) & Semantics COM016000 COMPUTERS / Computer Vision & Pattern Recognition Computer vision COMPUTERS / Image Processing Java (Computer program language) Machine learning Neural networks (Computer science) |
| Subtitle | Implement Machine Learning and Neural Network Methodologies to Perform Computer Vision-Related Tasks |
| TableOfContents | The power of 1 x 1 convolutions and the inception network -- Applying transfer learning -- Neural networks -- Building an animal image classification - using transfer learning and VGG-16 architecture -- Summary -- Chapter 4: Real-Time Object Detection -- Resolving object localization -- Labeling and defining data for localization -- Object localization prediction layer -- Landmark detection -- Object detection with the sliding window solution -- Disadvantages of sliding windows -- Convolutional sliding window -- Detecting objects with the YOLO algorithm -- Max suppression and anchor boxes -- Max suppression -- Anchor boxes -- Building a real-time video, car, and pedestrian detection application -- Architecture of the application -- YOLO V2-optimized architecture -- Coding the application -- Summary -- Chapter 5: Creating Art with Neural Style Transfer -- What are convolution network layers learning? -- Neural style transfer -- Minimizing the cost function -- Applying content cost function -- Applying style cost function -- How to capture the style -- Style cost function -- Building a neural network that produces art -- Summary -- Chapter 6: Face Recognition -- Problems in face detection -- Face verification versus face recognition -- Face verification -- Face recognition -- One-shot learning problem -- Similarity function -- Differentiating inputs with Siamese networks -- Learning with Siamese networks -- Exploring triplet loss -- Choosing the triplets -- Binary classification -- Binary classification cost function -- Building a face recognition Java application -- Summary -- Other Books You May Enjoy -- Index Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributor -- Table of Contents -- Preface -- Chapter 1: Introduction to Computer Vision and Training Neural Networks -- The computer vision state -- The importance of data in deep learning algorithms -- Exploring neural networks -- Building a single neuron -- Building a single neuron with multiple outputs -- Building a neural network -- How does a neural network learn? -- Learning neural network weights -- Updating the neural network weights -- Advantages of deep learning -- Organizing data and applications -- Organizing your data -- Bias and variance -- Computational model efficiency -- Effective training techniques -- Initializing the weights -- Activation functions -- Optimizing algorithms -- Configuring the training parameters of the neural network -- Representing images and outputs -- Multiclass classification -- Building a handwritten digit recognizer -- Testing the performance of the neural network -- Summary -- Chapter 2: Convolutional Neural Network Architectures -- Understanding edge detection -- What is edge detection? -- Vertical edge detection -- Horizontal edge detection -- Edge detection intuition -- Building a Java edge detection application -- Types of filters -- Basic coding -- Convolution on RGB images -- Working with convolutional layers' parameters -- Padding -- Stride -- Pooling layers -- Max pooling -- Average pooling -- Pooling on RGB images -- Pooling characteristics -- Building and training a Convolution Neural Network -- Why convolution? -- Improving the handwritten digit recognition application -- Summary -- Chapter 3: Transfer Learning and Deep CNN Architectures -- Working with classical networks -- LeNet-5 -- AlexNet -- VGG-16 -- Using residual networks for image recognition -- Deep network performance -- ResNet-50 Hands-On Java Deep Learning for Computer Vision: Implement machine learning and neural network methodologies to perform computer vision-related tasks |
| Title | Hands-On Java Deep Learning for Computer Vision |
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