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

What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support

1. The Groundwork – Julia's Environment

Julia is different
Setting up the environment
Using REPL
Using Jupyter Notebook
Package management
Parallel computation using Julia
Julia's key feature – multiple dispatch
Facilitating language interoperability
Summary
References

2. Data Munging FREE CHAPTER

What is data munging?
What is a DataFrame?
Summary
References

3. Data Exploration

Sampling
Inferring column types
Basic statistical summaries
Scalar statistics
Measures of variation
Scatter matrix and covariance
Computing deviations
Rankings
Counting functions
Histograms
Correlation analysis
Summary
References

4. Deep Dive into Inferential Statistics

Installation
Understanding the sampling distribution
Understanding the normal distribution
Type hierarchy in Distributions.jl
Univariate distributions
Truncated distributions
Understanding multivariate distributions
Understanding matrixvariate distributions
Distribution fitting
Confidence interval
Understanding z-score
Understanding the significance of the P-value
Summary
References

5. Making Sense of Data Using Visualization

Difference between using and importall
Pyplot for Julia
Unicode plots
Visualizing using Vega
Data visualization using Gadfly
Summary
References

6. Supervised Machine Learning

What is machine learning?
Machine learning – the process
Understanding decision trees
Supervised learning using Naïve Bayes
Summary
References

7. Unsupervised Machine Learning

Understanding clustering
K-means clustering
Summary
References

8. Creating Ensemble Models

What is ensemble learning?
Random forests
Implementation in Julia
Why is ensemble learning superior?
Summary
References

9. Time Series

What is forecasting?
What is TimeSeries?
Implementation in Julia
Summary
References

10. Collaborative Filtering and Recommendation System

What is a recommendation system?
Association rule mining
Content-based filtering
Collaborative filtering
Building a movie recommender system
Summary

11. Introduction to Deep Learning

Revisiting linear algebra
Probability and information theory
Differences between machine learning and deep learning
Implementation in Julia
Summary
References

References

http://docs.julialang.org/en/release-0.4/manual/
https://github.com/pluskid/Mocha.jl
http://psych.utoronto.ca/users/reingold/courses/ai/nn.html
https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/DeepLearning-NowPublishing-Vol7-SIG-039.pdf
http://www.deeplearningbook.org/contents/intro.html
http://deeplearning.net/tutorial/deeplearning.pdf

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Table of Contents (12) Chapters

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Table of Contents (12) Chapters

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