Book Image

Julia for Data Science

By : Anshul Joshi
2 (1)
Book Image

Julia for Data Science

2 (1)
By: Anshul Joshi

Overview of this book

Julia is a fast and high performing language that's perfectly suited to data science with a mature package ecosystem and is now feature complete. It is a good tool for a data science practitioner. There was a famous post at Harvard Business Review that Data Scientist is the sexiest job of the 21st century. (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century). This book will help you get familiarised with Julia's rich ecosystem, which is continuously evolving, allowing you to stay on top of your game. This book contains the essentials of data science and gives a high-level overview of advanced statistics and techniques. You will dive in and will work on generating insights by performing inferential statistics, and will reveal hidden patterns and trends using data mining. This has the practical coverage of statistics and machine learning. You will develop knowledge to build statistical models and machine learning systems in Julia with attractive visualizations. You will then delve into the world of Deep learning in Julia and will understand the framework, Mocha.jl with which you can create artificial neural networks and implement deep learning. This book addresses the challenges of real-world data science problems, including data cleaning, data preparation, inferential statistics, statistical modeling, building high-performance machine learning systems and creating effective visualizations using Julia.
Table of Contents (17 chapters)
Julia for Data Science
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface

Differences between machine learning and deep learning


Machine learning and deep learning intend to accomplish the same objective, but, they are distinctive and amount to various thoughts. Machine learning is the most major of the two and scientists and mathematicians have been doing research on it for a few decades now. Deep learning is a comparatively new idea. Deep learning is based on learning via neural networks (multiple layers) to achieve the goal. Understanding the difference between the two is important to know where we should apply deep learning and which problems can be solved using machine learning.

It was understood that a more intense approach to construct pattern recognition algorithms is achieved by utilizing the information that can be effortlessly mined relying only upon the area and the deciding objective.

For instance, in image recognition we accumulate various pictures and expand the algorithm on that. Utilizing the information as a part of these pictures, our model can...