Hands-On Java Deep Learning for Computer Vision
Klevis Ramo更新时间：2021-07-02 13:26:14
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Title Page
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Hands-On Java Deep Learning for Computer Vision
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Preface
Who this book is for
What this book covers
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Conventions used
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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
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
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
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
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
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
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
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更新时间：2021-07-02 13:26:14