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Neural Network Projects with Python

You're reading from   Neural Network Projects with Python The ultimate guide to using Python to explore the true power of neural networks through six projects

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789138900
Length 308 pages
Edition 1st Edition
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Author (1):
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James Loy James Loy
Author Profile Icon James Loy
James Loy
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Table of Contents (10) Chapters Close

Preface 1. Machine Learning and Neural Networks 101 2. Predicting Diabetes with Multilayer Perceptrons FREE CHAPTER 3. Predicting Taxi Fares with Deep Feedforward Networks 4. Cats Versus Dogs - Image Classification Using CNNs 5. Removing Noise from Images Using Autoencoders 6. Sentiment Analysis of Movie Reviews Using LSTM 7. Implementing a Facial Recognition System with Neural Networks 8. What's Next? 9. Other Books You May Enjoy

Technical requirements

The key Python libraries required for this chapter are:

  • matplotlib 3.0.2
  • Keras 2.2.4
  • Numpy 1.15.2
  • Piexif 1.1.2
To download the dataset required for this project, please refer to the directions at https://github.com/PacktPublishing/Neural-Network-Projects-with-Python/blob/master/Chapter04/how_to_download_the_dataset.txt.

The code for this chapter can be found in the GitHub repository for the book at https://github.com/PacktPublishing/Neural-Network-Projects-with-Python.

To download the code into your computer, you may run the following git clone command:

$ git clone https://github.com/PacktPublishing/Neural-Network-Projects-with-Python.git

After the process is complete, there will be a folder titled Neural-Network-Projects-with-Python. Enter the folder by running the following:

$ cd Neural-Network-Projects-with-Python

To install the required Python libraries...

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