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You're reading from Hands-On Deep Learning Architectures with Python Create deep neural networks to solve computational problems using TensorFlow and Keras

Product type Paperback

Published in Apr 2019

Publisher Packt

ISBN-13 9781788998086

Length 316 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (2):

Saransh Mehta

Yuxi (Hayden) Liu

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Table of Contents (15) Chapters

Preface

1. Section 1: The Elements of Deep Learning FREE CHAPTER

2. Getting Started with Deep Learning

3. Deep Feedforward Networks

4. Restricted Boltzmann Machines and Autoencoders

5. Section 2: Convolutional Neural Networks

6. CNN Architecture

7. Mobile Neural Networks and CNNs

8. Section 3: Sequence Modeling

9. Recurrent Neural Networks

10. Section 4: Generative Adversarial Networks (GANs)

11. Generative Adversarial Networks

12. Section 5: The Future of Deep Learning and Advanced Artificial Intelligence

13. New Trends of Deep Learning

14. Other Books You May Enjoy

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Who this book is for

If you're a data scientist, machine learning developer/engineer, deep learning practitioner, or are curious about the field of 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 most out of this book.

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Authors (2)

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.

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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.

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