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 |
| Témata: | |
| ISBN: | 1789613965, 9781789613964 |
| On-line přístup: | Získat plný text |
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- 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