Hands-On Deep Learning Architectures with Python

By : Yuxi (Hayden) Liu, Mehta

4.5 (2)

Buy this Book

Hands-On Deep Learning Architectures with Python

4.5 (2)

By: Yuxi (Hayden) Liu, Mehta

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Section 1: The Elements of Deep Learning

Getting Started with Deep Learning

Artificial intelligence

Deep learning

Building the fundamentals

TensorFlow and Keras

Summary

Deep Feedforward Networks

Evolutionary path to DFNs

Architecture of DFN

Training

Building our first DFN

Summary

Restricted Boltzmann Machines and Autoencoders

What are RBMs?

The evolution path of RBMs

RBM architectures and applications

What are autoencoders?

The evolution path of autoencoders

Autoencoders architectures and applications

Summary

Exercise

Acknowledgements

Section 2: Convolutional Neural Networks

CNN Architecture

Problem with deep feedforward networks

Evolution path to CNNs

Architecture of CNNs

Image classification with CNNs

Object detection with CNN

Summary

Mobile Neural Networks and CNNs

Evolution path to MobileNets

Architecture of MobileNets

MobileNet with Keras

MobileNetV2

Implementing MobileNetV2

Comparing the two MobileNets

SSD MobileNetV2

Summary

Section 3: Sequence Modeling

Recurrent Neural Networks

What are RNNs?

The evolution path of RNNs

RNN architectures and applications

Summary

Section 4: Generative Adversarial Networks (GANs)

Generative Adversarial Networks

What are GANs?

The evolution path of GANs

GAN architectures and implementations

InfoGANs

Summary

Section 5: The Future of Deep Learning and Advanced Artificial Intelligence

New Trends of Deep Learning

New trends in deep learning

Bayesian neural networks

Capsule networks

Meta-learning

Summary

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SSD MobileNetV2

The makers of MobileNetV2 also made real-time object detection possible for mobile devices. They introduced a combination of the SSD Object Detector and MobileNetV2, which is called SSDLite. Remember that in Chapter 4, CNN Architecture, we used ssd_mobilenetv2 for object detection. It is the same as SSDLite. The reason for choosing SSD is quite simple. SSD is built independent of the base network and hence the convolutions are replaced by depth-wise separable convolution. The first layer of SSDLite is attached to the expansion of layer 15 of MobileNetV2. Replacing standard convolutions with depth-wise separable convolution significantly reduces the number of parameters that are required by the network for object detection.

The following table shows a comparison of the number of parameters and multiplication operations required by the original SSD network...