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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 FREE CHAPTER 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

Favorite machine learning tools

In this book, I have used a lot of Python and Keras. Beyond that, there are also several machine learning tools that I consider to be useful:

  • Jupyter Notebook: Jupyter notebooks are interactive notebooks that are often used during the early stages of machine learning projects. The advantage of using Jupyter Notebooks is that it allows us to write interactive code iteratively. Unlike a .py Python file, code can be executed in chunks, and output (for example, graphs) can be displayed in line with the code.
  • Google Colab: Google Colab is a free cloud platform that allows us to write Jupyter Notebook code in the cloud. All changes are synced automatically, and teams can work collaboratively on the same notebook. The greatest advantage of Google Colab is that you can run code with GPU instances in the cloud, which are provided for free by Google! This...
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