In the previous chapter, we saw the architecture of a multi-neuron artificial neural network. But, the architecture consisted of only a single layer of neurons. Now think about the brain: does it have a single layer of neurons or multiple layers? Yes, the brain has multiple layers of neurons where the layers are connected one after the other. The inputs coming to the brain pass through an initial layer to extract low-level features and pass through consecutive layers to extract high-level features. The architecture of DFN is inspired by the layered structure of multiple neurons. The network has various layers stacked consecutively where the neuron outputs from previous layers are fed forward as inputs to the next layers (that's why the network is called feedforward network). Three types of layers are present in the architecture-input layer, hidden layer...
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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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