In this chapter, you will build our first deep learning network—deep deedforward networks (DFN). We will begin by discussing the evolutionary history of deep feedforward networks and then discuss the architecture of DFN. In any classification task, DFN plays an integral role. Apart from supporting the classification tasks, DFN standalone can be used both for regression and classification. Any deep learning network has a lot of elements like loss function, gradients, optimizers, and so on coming together to train the network. In this chapter, we will discuss these essential elements in detail. These elements will be common to all kinds of deep learning networks we are going to see in this book. We will also be demonstrating how to bring up and preprocess the data for training a deep learning network. You may find things a little difficult to understand...
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.
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Hands-On Deep Learning Architectures with Python
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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