Explore Products

Best Sellers

New Releases

Books

Videos

Audiobooks

Learning Hub

Free Learning

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

Yuxi (Hayden) Liu

Mehta

€18.99 per month

4.5 (2 Ratings)

Paperback Apr 2019 316 pages 1st Edition

Yuxi (Hayden) Liu

Mehta

€18.99 per month

4.5 (2 Ratings)

Paperback Apr 2019 316 pages 1st Edition

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

View table of contents

Preview Book

Download Code

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

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

Frequently bought together

Advanced Deep Learning with Python

Dec 2019 468 pages

4.8 (23)

eBook

€20.98 ~~€29.99~~

Hands-On Deep Learning Algorithms with Python

Jul 2019 512 pages

4.1 (13)

eBook

€15.99 ~~€23.99~~

Hands-On Deep Learning Architectures with Python

Apr 2019 316 pages

4.5 (2)

eBook

€13.98 ~~€19.99~~

Total € 91.97

€36.99

€29.99

€24.99

Total € 91.97

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

Other Books You May Enjoy

Leave a review - let other readers know what you think

Recommendations for you

Python Machine Learning By Example

Jul 2024 518 pages

4.9 (8)

eBook

€18.99 ~~€27.99~~

Unlocking Data with Generative AI and RAG

Sep 2024 346 pages

eBook

€15.99 ~~€23.99~~

Building LLM Powered Applications

May 2024 342 pages

4.2 (22)

eBook

€20.98 ~~€29.99~~

Python for Algorithmic Trading Cookbook

Aug 2024 404 pages

4.6 (15)

eBook

€24.99 ~~€35.99~~

Machine Learning with PyTorch and Scikit-Learn

Feb 2022 774 pages

4.4 (95)

eBook

€22.99 ~~€32.99~~

RAG-Driven Generative AI

Sep 2024 334 pages

4.5 (13)

eBook

€22.99 ~~€32.99~~

Artificial Intelligence Engines

Nov 2024 217 pages

eBook

€4.99 ~~€7.99~~

AI Product Manager's Handbook

Nov 2024 484 pages

eBook

Hands-On Reinforcement Learning with Python

Jun 2018 318 pages

2.6 (18)

eBook

€15.99 ~~€23.99~~

LLM Engineer's Handbook

Oct 2024 522 pages

4.8 (13)

eBook

€43.99

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

People who bought this also bought

Machine Learning Engineering with Python

Aug 2023 462 pages

4.6 (36)

eBook

€20.98 ~~€29.99~~

Deep Learning with TensorFlow and Keras – 3rd edition

Oct 2022 698 pages

4.6 (45)

eBook

€20.98 ~~€29.99~~

Modern Generative AI with ChatGPT and OpenAI Models

May 2023 286 pages

4.2 (34)

eBook

€20.98 ~~€29.99~~

Generative AI with LangChain

Dec 2023 368 pages

4 (34)

eBook

€20.98 ~~€29.99~~

Causal Inference and Discovery in Python

May 2023 456 pages

4.5 (49)

eBook

€15.99 ~~€23.99~~

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

FAQs

What is included in a Packt subscription?

A subscription provides you with full access to view all Packt and licnesed content online, this includes exclusive access to Early Access titles. Depending on the tier chosen you can also earn credits and discounts to use for owning content

How can I cancel my subscription?

To cancel your subscription with us simply go to the account page - found in the top right of the page or at https://subscription.packtpub.com/my-account/subscription - From here you will see the ‘cancel subscription’ button in the grey box with your subscription information in.

What are credits?

Credits can be earned from reading 40 section of any title within the payment cycle - a month starting from the day of subscription payment. You also earn a Credit every month if you subscribe to our annual or 18 month plans. Credits can be used to buy books DRM free, the same way that you would pay for a book. Your credits can be found in the subscription homepage - subscription.packtpub.com - clicking on ‘the my’ library dropdown and selecting ‘credits’.

What happens if an Early Access Course is cancelled?

Projects are rarely cancelled, but sometimes it's unavoidable. If an Early Access course is cancelled or excessively delayed, you can exchange your purchase for another course. For further details, please contact us here.

Where can I send feedback about an Early Access title?

If you have any feedback about the product you're reading, or Early Access in general, then please fill out a contact form here and we'll make sure the feedback gets to the right team.

Can I download the code files for Early Access titles?

We try to ensure that all books in Early Access have code available to use, download, and fork on GitHub. This helps us be more agile in the development of the book, and helps keep the often changing code base of new versions and new technologies as up to date as possible. Unfortunately, however, there will be rare cases when it is not possible for us to have downloadable code samples available until publication.

When we publish the book, the code files will also be available to download from the Packt website.

How accurate is the publication date?

The publication date is as accurate as we can be at any point in the project. Unfortunately, delays can happen. Often those delays are out of our control, such as changes to the technology code base or delays in the tech release. We do our best to give you an accurate estimate of the publication date at any given time, and as more chapters are delivered, the more accurate the delivery date will become.

How will I know when new chapters are ready?

We'll let you know every time there has been an update to a course that you've bought in Early Access. You'll get an email to let you know there has been a new chapter, or a change to a previous chapter. The new chapters are automatically added to your account, so you can also check back there any time you're ready and download or read them online.

I am a Packt subscriber, do I get Early Access?

Yes, all Early Access content is fully available through your subscription. You will need to have a paid for or active trial subscription in order to access all titles.

How is Early Access delivered?

Early Access is currently only available as a PDF or through our online reader. As we make changes or add new chapters, the files in your Packt account will be updated so you can download them again or view them online immediately.

How do I buy Early Access content?

Early Access is a way of us getting our content to you quicker, but the method of buying the Early Access course is still the same. Just find the course you want to buy, go through the check-out steps, and you’ll get a confirmation email from us with information and a link to the relevant Early Access courses.

What is Early Access?

Keeping up to date with the latest technology is difficult; new versions, new frameworks, new techniques. This feature gives you a head-start to our content, as it's being created. With Early Access you'll receive each chapter as it's written, and get regular updates throughout the product's development, as well as the final course as soon as it's ready.We created Early Access as a means of giving you the information you need, as soon as it's available. As we go through the process of developing a course, 99% of it can be ready but we can't publish until that last 1% falls in to place. Early Access helps to unlock the potential of our content early, to help you start your learning when you need it most. You not only get access to every chapter as it's delivered, edited, and updated, but you'll also get the finalized, DRM-free product to download in any format you want when it's published. As a member of Packt, you'll also be eligible for our exclusive offers, including a free course every day, and discounts on new and popular titles.

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

What do you get with a Packt Subscription?