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Learning Data Mining with Python

You're reading from   Learning Data Mining with Python Use Python to manipulate data and build predictive models

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781787126787
Length 358 pages
Edition 2nd Edition
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Data Mining FREE CHAPTER 2. Classifying with scikit-learn Estimators 3. Predicting Sports Winners with Decision Trees 4. Recommending Movies Using Affinity Analysis 5. Features and scikit-learn Transformers 6. Social Media Insight using Naive Bayes 7. Follow Recommendations Using Graph Mining 8. Beating CAPTCHAs with Neural Networks 9. Authorship Attribution 10. Clustering News Articles 11. Object Detection in Images using Deep Neural Networks 12. Working with Big Data 13. Next Steps...

An Introduction to TensorFlow

TensorFlow is a graph computation library designed by engineers at Google, and is starting to power many of Google's recent advances in deep learning and artificial intelligence.

A graph computation library has two steps. They are listed below:

  1. Defining the sequence (or more complex graphs) of operations that take the input data, operate on it, and convert to outputs.
  2. Compute on the graph obtained from step 1 with a given input.

Many programmers don't use this type of programming day-to-day, but most of them interact with a related system that does. Relational databases, specifically SQL-based ones, use a similar concept called the declarative paradigm. While a programmer might define a SELECT query on a database with a WHERE clause, the database interprets that and creates an optimised query based on a number of factors, such as whether the WHERE clause is applied to a...

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