Explore advanced deep learning architectures using various datasets and frameworks
Implement deep architectures for neural network models such as CNN, RNN, GAN, and many more
Discover design patterns and different challenges for various deep learning architectures

Description

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.

What you will learn

Implement CNNs, RNNs, and other commonly used architectures with Python Explore architectures such as VGGNet, AlexNet, and GoogLeNet Build deep learning architectures for AI applications such as face and image recognition, fraud detection, and many more Understand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architecture Understand deep learning architectures for mobile and embedded systems

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Estimated delivery fee Deliver to Spain

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Product Details

Publication date : Apr 30, 2019

Length 316 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781788998086

Vendor :

Google

Category :

Data

Languages :

Python (Expert)

Concepts :

Deep Learning

Tools :

Scikit-learn (Intermediate) , TensorFlow (Intermediate)

15 Chapters

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

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

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About the authors

Saransh Mehta

Saransh Mehta has cross-domain experience of working with texts, images, and audio using deep learning. He has been building artificial, intelligence-based solutions, including a generative chatbot, an attendee-matching recommendation system, and audio keyword recognition systems for multiple start-ups. He is very familiar with the Python language, and has extensive knowledge of deep learning libraries such as TensorFlow and Keras. He has been in the top 10% of entrants to deep learning challenges hosted by Microsoft and Kaggle.

See other products by Saransh Mehta

Mr. Yuxi (Hayden) Liu

Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. He is developing and improving machine learning models and systems for ads optimization on the largest search engine in the world.

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Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras

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