Title Page Copyright and Credits Hands-On Java Deep Learning for Computer Vision About Packt Why subscribe? Packt.com Contributor About the author Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews 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 Other Books You May Enjoy Leave a review - let other readers know what you think