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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Comparing the two MobileNets

MobileNetV2 has introduced significant changes in the architecture of MobileNet. Were the changes worth making? How much better is MobileNetV2 than MobileNet in regards to performance? We can compare the models in terms of the number of multiplication operations required for one inference, which is commonly known as MACs (number of multiply-accumulates). The higher the MAC value, the heavier the network is. We can also compare the models in terms of the number of parameters in the model. The following table shows the MACs and the number of parameters for both MobileNet and MobileNetV2:

Network Number of Parameters MACs/ MAdds
MobileNet V1 4.2M 575M
MobileNet V2 3.4M 300M

 

We can also compare the models in terms of memory that's required for the different number of channels and resolution. The following table provides...

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