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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 5. Retrieving, Processing, and Storing Data 6. Data Visualization 7. Signal Processing and Time Series 8. Working with Databases 9. Analyzing Textual Data and Social Media 10. Predictive Analytics and Machine Learning 11. Environments Outside the Python Ecosystem and Cloud Computing 12. Performance Tuning, Profiling, and Concurrency A. Key Concepts
B. Useful Functions C. Online Resources
Index

Neural networks

Artificial Neural Networks (ANN) are models inspired by the animal brain (highly evolved animals). A neural network is a network of neurons—units with inputs and outputs. For example, the input can be a value related to the pixel of an image and the output of a neuron can be passed to another neuron and so on, thus creating a multilayered network. Neural networks contain adaptive elements making them suitable to deal with nonlinear models and pattern recognition problems. We will again try to predict whether it is going to rain based on day-of-the-year and previous day values. Let's use the theanets Python library, which can be installed as follows:

$ sudo pip install theanets
$ pip freeze|grep theanets
theanets==0.2.0

One of the technical reviewers encountered an error, which was resolved by updating NumPy and SciPy. We first create an Experiment corresponding to a neural network and then train the network. Create a network with two input neurons and one output...

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