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Julia for Data Science

You're reading from   Julia for Data Science high-performance computing simplified

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Anshul Joshi Anshul Joshi
Author Profile Icon Anshul Joshi
Anshul Joshi
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Table of Contents (12) Chapters Close

Preface 1. The Groundwork – Julia's Environment 2. Data Munging FREE CHAPTER 3. Data Exploration 4. Deep Dive into Inferential Statistics 5. Making Sense of Data Using Visualization 6. Supervised Machine Learning 7. Unsupervised Machine Learning 8. Creating Ensemble Models 9. Time Series 10. Collaborative Filtering and Recommendation System 11. Introduction to Deep Learning

Probability and information theory

Probability theory is a scientific system for speaking to questionable explanations. It gives a method for evaluating instability and adages for inferring new indeterminate statements.

In applications of AI, we utilize probability theory as follows:

  • The laws of probability define how AI frameworks ought to reason, so algorithms are designed to figure or approximate different expressions inferred on utilizing probability theory
  • Probability and statistics can be utilized to hypothetically investigate the behavior of proposed AI frameworks

While probability theory permits us to put forth indeterminate expressions and reason within the sight of uncertainty, data permits us to measure the degree of uncertainty in a probability distribution.

Why probability?

Machine learning makes substantial utilization of probability theory unlike other branches of computer science that are mainly dependent on the deterministic nature of the computer system:

  • This is on the grounds...
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