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Deep Learning Quick Reference

You're reading from   Deep Learning Quick Reference Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788837996
Length 272 pages
Edition 1st Edition
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Author (1):
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Mike Bernico Mike Bernico
Author Profile Icon Mike Bernico
Mike Bernico
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Table of Contents (15) Chapters Close

Preface 1. The Building Blocks of Deep Learning FREE CHAPTER 2. Using Deep Learning to Solve Regression Problems 3. Monitoring Network Training Using TensorBoard 4. Using Deep Learning to Solve Binary Classification Problems 5. Using Keras to Solve Multiclass Classification Problems 6. Hyperparameter Optimization 7. Training a CNN from Scratch 8. Transfer Learning with Pretrained CNNs 9. Training an RNN from scratch 10. Training LSTMs with Word Embeddings from Scratch 11. Training Seq2Seq Models 12. Using Deep Reinforcement Learning 13. Generative Adversarial Networks 14. Other Books You May Enjoy

Training LSTMs with Word Embeddings from Scratch

So far, we've seen examples of the application of deep learning in structured data, image data, and even time series data. It seems only right to move on to natural language processing (NLP) as the next stop on our tour. The connection between machine learning and human language is a fascinating one. Deep learning has exponentially accelerated the pace at which this field is moving, as it has with computer vision. Let's start with a brief overview of NLP and some of the tasks we'll be taking on in this chapter.

We will also cover the following topics in this chapter:

  • An introduction to natural language processing
  • Vectorizing text
  • Word embedding
  • Keras embedding layer
  • 1D CNNs for natural language processing
  • Case studies for document classifications
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