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Deep Learning By Example

You're reading from Deep Learning By Example

Product type Book

Published in Feb 2018

Publisher Packt

ISBN-13 9781788399906

Pages 450 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (18) Chapters

Preface

1. Data Science - A Birds' Eye View

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

17. Other Books You May Enjoy

Leave a review - let other readers know what you think

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...

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