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Julia for Data Science

You're reading from  Julia for Data Science

Product type Book
Published in Sep 2016
Publisher Packt
ISBN-13 9781785289699
Pages 346 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Anshul Joshi Anshul Joshi
Profile icon Anshul Joshi
LinkedIn
Anshul Joshi is a data scientist with experience in recommendation systems, predictive modeling, neural networks, and high performance computing. His research interests encompass deep learning, artificial intelligence, and computational physics. Most of the time, he can be caught exploring GitHub or trying anything new he can get his hands on. You can also follow his personal blog.
See other products by Anshul Joshi

Table of Contents (17) Chapters

Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
1. The Groundwork – Julia's Environment 2. Data Munging 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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Authors (1)

Profile icon Anshul Joshi
LinkedIn
Anshul Joshi is a data scientist with experience in recommendation systems, predictive modeling, neural networks, and high performance computing. His research interests encompass deep learning, artificial intelligence, and computational physics. Most of the time, he can be caught exploring GitHub or trying anything new he can get his hands on. You can also follow his personal blog.
See other products by Anshul Joshi

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