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Neural Network Projects with Python

You're reading from  Neural Network Projects with Python

Product type Book
Published in Feb 2019
Publisher Packt
ISBN-13 9781789138900
Pages 308 pages
Edition 1st Edition
Languages
Author (1):
James Loy James Loy
Profile icon James Loy

Table of Contents (10) Chapters

Preface 1. Machine Learning and Neural Networks 101 2. Predicting Diabetes with Multilayer Perceptrons 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 Python libraries required for this chapter are as follows:

  • matplotlib 3.0.2
  • Keras 2.2.4
  • seaborn 0.9.0
  • scikit-learn 0.20.2

The code for this chapter can be found in the GitHub repository for the book.

To download the code onto 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 entitled Neural-Network-Projects-with-Python. Enter the folder by running the following:

$ cd Neural-Network-Projects-with-Python

To install the required Python libraries in a virtual environment, run the following command:

$ conda env create -f environment.yml

Note that you should have installed Anaconda on your computer first, before running this command. To enter the virtual environment, run the following command:

$ conda activate...
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