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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

Introduction

Let's say you're working with text data and your objective is to build a model that checks whether a sentence is grammatically correct. Consider the following sentence: "words? while sequence be this solved of can the ignoring". The question didn't make sense, right? Well, how about the following? "Can this be solved while ignoring the sequence of words?"

Suddenly, the text makes complete sense. What do we acknowledge, then, about working with text data? That sequence matters.

In the task of assessing whether a given sentence is grammatically correct, the sequence is important. Sequence-agnostic models would fail terribly at the task. The nature of the task requires you to analyze the sequence of the terms.

In the previous chapter, we worked with text data, discussing ideas around representation and creating our own word vectors. Text and natural language data have another important characteristic – they have a sequence...

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