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Deep Learning By Example

You're reading from   Deep Learning By Example A hands-on guide to implementing advanced machine learning algorithms and neural networks

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788399906
Length 450 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
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Toc

Table of Contents (18) Chapters Close

Preface 1. Data Science - A Birds' Eye View 2. Data Modeling in Action - The Titanic Example FREE CHAPTER 3. Feature Engineering and Model Complexity – The Titanic Example Revisited 4. Get Up and Running with TensorFlow 5. TensorFlow in Action - Some Basic Examples 6. Deep Feed-forward Neural Networks - Implementing Digit Classification 7. Introduction to Convolutional Neural Networks 8. Object Detection – CIFAR-10 Example 9. Object Detection – Transfer Learning with CNNs 10. Recurrent-Type Neural Networks - Language Modeling 11. Representation Learning - Implementing Word Embeddings 12. Neural Sentiment Analysis 13. Autoencoders – Feature Extraction and Denoising 14. Generative Adversarial Networks 15. Face Generation and Handling Missing Labels 16. Implementing Fish Recognition 17. Other Books You May Enjoy

Summary

In this chapter, we introduced one of the most widely used best practices of deep learning. TL is a very exciting tool that you can use to get deep learning architectures to learn from your small dataset, but make sure you use it in the right way.

Next up, we are going to introduce a widely used deep learning architecture for natural language processing. These recurrent-type architectures have achieved a breakthrough in most NLP domains: machine translation, speech recognition, language modeling, and sentiment analysis.

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