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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
Author (1):
Anshul Joshi Anshul Joshi
Profile icon 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

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...

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