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

Yuxi (Hayden) Liu

Mehta

AU$45.99

4.5 (2 Ratings)

Paperback Apr 2019 316 pages 1st Edition

eBook

AU$24.99 ~~AU$36.99~~

Renews at AU$24.99p/m

Yuxi (Hayden) Liu

Mehta

AU$45.99

4.5 (2 Ratings)

Paperback Apr 2019 316 pages 1st Edition

eBook

AU$24.99 ~~AU$36.99~~

Renews at AU$24.99p/m

eBook

AU$24.99 ~~AU$36.99~~

Renews at AU$24.99p/m

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Key benefits

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.

Who is this book for?

If you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book

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

Estimated delivery fee Deliver to Australia

Economy delivery 7 - 10 business days

AU$19.95

What do you get with Print?

Instant access to your digital eBook copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Estimated delivery fee Deliver to Australia

Economy delivery 7 - 10 business days

AU$19.95

Product Details

Publication date : Apr 30, 2019

Length: 316 pages

Edition : 1st

Language : English

ISBN-13 : 9781788998086

Vendor :

Google

Category :

Data

Languages :

Python

Concepts :

Deep Learning

Tools :

Keras

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14 Chapters

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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Customer reviews

Rating distribution

4.5

(2 Ratings)

5 star 50%

4 star 50%

3 star 0%

2 star 0%

1 star 0%

andre luis Apr 25, 2022

Este livro faz o balanceamento da teoria e da prática, fazendo a conexão entre ambos na medida certa, nem exagerando na teoria como outras publicações o fazem, nem carecendo de explicação mínima para os comandos utilizados, que são o foco desta obra ( Tensorflow e Keras ).Obviamente é necessário um conhecimento prévio mínimo por parte do leitor sobre o assunto 'redes neurais', e o livro não desperdiça muitas páginas com essa introdução; apresentando assim no máximo uma revisão da teoria.

Amazon Verified review

Anonymus Jul 31, 2019

All programs are implemented using tensorflow and keras.Some of the algorithms are not explained from the scratchwhich is very important to learn deeplearning indepth

Amazon Verified review

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About the authors

Yuxi (Hayden) Liu

Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval. He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages.

See other products by Yuxi (Hayden) Liu

Mehta

Chintan Mehta is a co-founder of KNOWARTH Technologies and heads the cloud/RIMS/DevOps team. He has rich, progressive experience in server administration of Linux, AWS Cloud, DevOps, RIMS, and on open source technologies. He is also an AWS Certified Solutions Architect. Chintan has authored MySQL 8 for Big Data, Mastering Apache Solr 7.x, MySQL 8 Administrator's Guide, and Hadoop Backup and Recovery Solutions. Also, he has reviewed Liferay Portal Performance Best Practices and Building Serverless Web Applications.

See other products by Mehta

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What is the delivery time and cost of print books?

Shipping Details

USA:

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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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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