Until now, in this book, we have been mostly using convolutional neural networks (CNNs) for classification. Classification classifies the whole image into one of the classes with respect to the entity having the maximum probability of detection in the image. But what if there is not one, but multiple entities of interest and we want to have the image associated with all of them? One way to do this is to use tags instead of classes, where these tags are all classes of the penultimate Softmax classification layer with probability above a given threshold. However, the probability of detection here varies widely by size and placement of entity, and from the following image, we can actually say, How confident is the model that the identified entity is the one that is claimed? What if we are very confident that there is an...
Practical Convolutional Neural Networks
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Practical Convolutional Neural Networks
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Overview of this book
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.
This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.
Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision.
By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
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Current Title:
Practical Convolutional Neural Networks
Table of Contents (11 chapters)
Preface
Free Chapter
Deep Neural Networks – Overview
Introduction to Convolutional Neural Networks
Build Your First CNN and Performance Optimization
Popular CNN Model Architectures
Transfer Learning
Autoencoders for CNN
Object Detection and Instance Segmentation with CNN
GAN: Generating New Images with CNN
Attention Mechanism for CNN and Visual Models
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