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The Deep Learning Workshop

You're reading from   The Deep Learning Workshop Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781839219856
Length 474 pages
Edition 1st Edition
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Authors (5):
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Nipun Sadvilkar Nipun Sadvilkar
Author Profile Icon Nipun Sadvilkar
Nipun Sadvilkar
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Mohan Kumar Silaparasetty Mohan Kumar Silaparasetty
Author Profile Icon Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
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Toc

Table of Contents (9) Chapters Close

Preface
1. Building Blocks of Deep Learning 2. Neural Networks FREE CHAPTER 3. Image Classification with Convolutional Neural Networks (CNNs) 4. Deep Learning for Text – Embeddings 5. Deep Learning for Sequences 6. LSTMs, GRUs, and Advanced RNNs 7. Generative Adversarial Networks Appendix

Distributed Representation for Text

Why are word embeddings so popular? Why are we claiming they are amazingly powerful? What makes them so special? To understand and appreciate word embeddings, we need to acknowledge the shortcomings of the representations so far.

The terms "footpath" and "sidewalk" are synonyms. Do you think the approaches we've discussed so far will be able to capture this information? Well, you could manually go in and replace "sidewalk" with "footpath" so that both have the same token eventually, but can you do this for all possible synonyms in the language?

The terms "hot" and "cold" are antonyms. Do the previous Bag-of-Words representations capture this? What about "dog" being a type of "animal"? "Cockpit" being a part of a "plane"? Differentiating between a dog's bark and a tree's bark? Can you handle all these cases manually?

All the...

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