In this chapter, we will discuss an important class of deep learning network for images called convolutional neural networks (CNNs). The majority of the deep learning models built for image-related tasks, such as image recognition, classification, object detection, and so on, involve CNNs as their primary network. CNNs allow us to process the incoming data in a three-dimensional volume rather than a single dimension vector. Although CNNs are a class of neural networks (made up of weights, layers, and loss function), there are a lot of architectural differences to deep feedforward networks, which we will explain in this chapter. Just to give you an idea of how powerful CNNs can be, the ResNet CNN architecture achieved a top-error rate of 3.57% at the world famous image classification challenge—ILSVRC. This performance beats the human vision perception...
Hands-On Deep Learning Architectures with Python
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Hands-On Deep Learning Architectures with Python
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Overview of this book
Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.
Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.
By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.
Table of Contents (15 chapters)
Preface
Getting Started with Deep Learning
Deep Feedforward Networks
Restricted Boltzmann Machines and Autoencoders
Section 2: Convolutional Neural Networks
CNN Architecture
Mobile Neural Networks and CNNs
Section 3: Sequence Modeling
Recurrent Neural Networks
Section 4: Generative Adversarial Networks (GANs)
Generative Adversarial Networks
Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
New Trends of Deep Learning
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