Book Image

Hands-On Deep Learning Architectures with Python

By : Yuxi (Hayden) Liu, Saransh Mehta
Book Image

Hands-On Deep Learning Architectures with Python

By: Yuxi (Hayden) Liu, Saransh Mehta

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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Table of Contents (15 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: The Elements of Deep Learning
Section 1: The Elements of Deep Learning
2
Getting Started with Deep Learning
Getting Started with Deep Learning
Artificial intelligence
Deep learning
Building the fundamentals
TensorFlow and Keras
Summary
3
Deep Feedforward Networks
Deep Feedforward Networks
Evolutionary path to DFNs
Architecture of DFN
Training
Building our first DFN
Summary
4
Restricted Boltzmann Machines and Autoencoders
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
5
Section 2: Convolutional Neural Networks
Section 2: Convolutional Neural Networks
6
CNN Architecture
CNN Architecture
Problem with deep feedforward networks
Evolution path to CNNs
Architecture of CNNs
Image classification with CNNs
Object detection with CNN
Summary
7
Mobile Neural Networks and CNNs
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
8
Section 3: Sequence Modeling
Section 3: Sequence Modeling
9
Recurrent Neural Networks
Recurrent Neural Networks
What are RNNs?
The evolution path of RNNs
RNN architectures and applications
Summary
10
Section 4: Generative Adversarial Networks (GANs)
Section 4: Generative Adversarial Networks (GANs)
11
Generative Adversarial Networks
Generative Adversarial Networks
What are GANs?
The evolution path of GANs
GAN architectures and implementations
InfoGANs
Summary
12
Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
13
New Trends of Deep Learning
New Trends of Deep Learning
New trends in deep learning
Bayesian neural networks
Capsule networks
Meta-learning
Summary
14
Other Books You May Enjoy
Other Books You May Enjoy
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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...

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