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Julia for Data Science

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

Product type Paperback

Published in Sep 2016

Publisher Packt

ISBN-13 9781785289699

Length 346 pages

Edition 1st Edition

Languages

Julia

Concepts

Data Science

Author (1):

Anshul Joshi

View More author details

Table of Contents (12) Chapters

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

Understanding the sampling distribution

The sampling distribution is the likelihood of gathering every possible statistic from a sample of a population that is taken randomly. Useful information can be derived using the sampling distribution without the complete knowledge of the population. Suppose we are calculating the sample mean but we don't know the population. Still, we can assume that the sample mean is within a certain number of standard deviations of the population mean.

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Authors (1)

Anshul Joshi

Anshul Joshi

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.

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