Seven Must-Read Data Science & Deep Learning Articles This Month

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The demand for data scientist experts is projected to grow by 15% by 2020, according to a study by IBM and the demand for artificial intelligence and machine learning experts is expected to rise as well. Companies are increasingly hiring AI experts and freelance data scientists to make up for the shortage of in-house talent, and universities all over the world are introducing machine learning and analytics courses.

It is imperative that everyone who uses data or is interested in Deep Learning – especially businesses and researchers – keep themselves up to date with the latest developments in the field. Here is a compilation of seven useful and informative resources as recent as August 2017, that will help you you stay on top of your game.

1. DataViz as History: Maps Illustrating The Solar Eclipses of the 20th Century
(Michael Sandberg, Dataviz blog)

2. 7 Predictions for the Analytics Market
(Taner Akcok, Becoming Human)

3. How to Plan and Run Machine Learning Experiments Systematically
(Jason Brownlee, Machine Learning Mastery)

4. Using Machine Learning to Improve Patient Care 
(Rachel Gordon, MIT News)

5. NASA is using Intel’s deep learning to build better moon maps
(Brian Heater, Tech Crunch)

6. A comprehensive Introduction to Deep Learning (Video)
(DeepLearning.TV)

7. Analyzing cryptocurrency markets using Python
(Patrick Triest’s blog)

Do you have suggestions of articles or resources that we can add to the list? We’d love to hear from you. Write to us in the comments below or Tweet to us.


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Ramya Sriram manages digital content and communications at Kolabtree (kolabtree.com), the world's largest freelancing platform for scientists. She has over a decade of experience in publishing, advertising and digital content creation.

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