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

Product type Paperback

Published in Feb 2018

Publisher Packt

ISBN-13 9781788399906

Length 450 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Author (1):

Menshawy

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Table of Contents (23) Chapters

Title Page

Packt Upsell

Contributors

Preface

1. Data Science - A Birds' Eye View FREE CHAPTER

2. Data Modeling in Action - The Titanic Example

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

Code for fish recognition

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Index

Sentiment analysis – model implementation

We have seen all the bits and pieces of how to implement a stacked version of the LSTM variation of RNNs. To make things a bit exciting, we are going to use a higher level API called Keras.

Keras

"Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research." – Keras website

So, Keras is just a wrapper around TensorFlow and other deep learning frameworks. It's really good for prototyping and getting things built very quickly, but on the other hand, it gives you less control over your code. We'll take a chance to implement this sentiment analysis model in Keras so that you get a hands-on implementation in both TensorFlow and Keras. You can use Keras for fast prototyping and TensorFlow for a production-ready system.

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