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Deep Learning Essentials

You're reading from   Deep Learning Essentials Your hands-on guide to the fundamentals of deep learning and neural network modeling

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785880360
Length 284 pages
Edition 1st Edition
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Authors (3):
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Wei Di Wei Di
Author Profile Icon Wei Di
Wei Di
Anurag Bhardwaj Anurag Bhardwaj
Author Profile Icon Anurag Bhardwaj
Anurag Bhardwaj
Jianing Wei Jianing Wei
Author Profile Icon Jianing Wei
Jianing Wei
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Table of Contents (12) Chapters Close

Preface 1. Why Deep Learning? 2. Getting Yourself Ready for Deep Learning FREE CHAPTER 3. Getting Started with Neural Networks 4. Deep Learning in Computer Vision 5. NLP - Vector Representation 6. Advanced Natural Language Processing 7. Multimodality 8. Deep Reinforcement Learning 9. Deep Learning Hacks 10. Deep Learning Trends 11. Other Books You May Enjoy

Basics of linear algebra

One of the most fundamental skills required to get oneself setup with deep learning is a foundational understanding of linear algebra. Though linear algebra itself is a vast subject, and covering it in full is outside the scope of this book, we will go through some important aspects of linear algebra in this chapter. Hopefully, this will give you a sufficient understanding of some core concepts and how they interplay with deep learning methodologies.

Data representation

In this section, we will look at core data structures and representations used most commonly across different linear algebra tasks. This is not meant to be a comprehensive list at all but only serves to highlight some of the prominent...

You have been reading a chapter from
Deep Learning Essentials
Published in: Jan 2018
Publisher: Packt
ISBN-13: 9781785880360
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