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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

Titanic example – model building and training

The sinking of the ship, Titanic, is one of the most infamous events in history. This incident led to the deaths of 1,502 passengers and crew out of 2,224. In this problem, we will use data science to predict whether the passenger will survive this tragedy or not and then test the performance of our model based on the actual statistics of the tragedy.

To follow up with the Titanic example, you need to do the following:

Download this repository in a ZIP file by clicking on https://github.com/ahmed-menshawy/ML_Titanic/archive/master.zip or execute from the terminal:
Git clone: https://github.com/ahmed-menshawy/ML_Titanic.git
Install [virtualenv]: (http://virtualenv.readthedocs.org/en/latest/installation.html)
Navigate to the directory where you unzipped or cloned the repo and create a virtual environment with virtualenv ml_titanic...

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