Generally, a neural network needs labeled examples to learn effectively. Unsupervised learning approaches to learn from unlabeled data have not worked very well. A generative adversarial network, or simply a GAN, is part of an unsupervised learning approach but based on differentiable generator networks. GANs were first invented by Ian Goodfellow and others in 2014. Since then they have become extremely popular. This is based on game theory and has two players or networks: a generator network and b) a discriminator network, both competing against each other. This dual network game theory-based approach vastly improved the process of learning from unlabeled data. The generator network produces fake data and passes it to a discriminator. The discriminator network also sees real data and predicts whether the data it receives is fake or...
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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