Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Deep Learning with TensorFlow 2 and Keras

You're reading from   Deep Learning with TensorFlow 2 and Keras Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API

Arrow left icon
Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781838823412
Length 646 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (3):
Arrow left icon
Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Sujit Pal Sujit Pal
Author Profile Icon Sujit Pal
Sujit Pal
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Neural Network Foundations with TensorFlow 2.0 2. TensorFlow 1.x and 2.x FREE CHAPTER 3. Regression 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Generative Adversarial Networks 7. Word Embeddings 8. Recurrent Neural Networks 9. Autoencoders 10. Unsupervised Learning 11. Reinforcement Learning 12. TensorFlow and Cloud 13. TensorFlow for Mobile and IoT and TensorFlow.js 14. An introduction to AutoML 15. The Math Behind Deep Learning 16. Tensor Processing Unit 17. Other Books You May Enjoy
18. Index

Distributed representations

Distributed representations attempt to capture the meaning of a word by considering its relations with other words in its context. The idea behind the distributed hypothesis is captured in this quote from J. R. Firth, a linguist who first proposed this idea:

"You shall know a word by the company it keeps."

How does this work? By way of example, consider the following pair of sentences:

Paris is the capital of France.

Berlin is the capital of Germany.

Even assuming no knowledge of world geography, the sentence pair implies some sort of relationship between the entities Paris, France, Berlin, and Germany that could be represented as:

"Paris" is to "France" as "Berlin" is to "Germany"

Distributed representations are based on the idea that there exists some transformation such that:

In other words, a distributed embedding space is one where words that are used in similar contexts...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime