) KDnuggets™ News 17:n08, Mar 1: Deep Learning Frameworks Dissected; Gartner Magic Quadrant for Data Science Platforms
The Anatomy of Deep Learning Frameworks; Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers; Data Science vs Fake News Contest; An Overview of Python Deep Learning Frameworks; Moving from R to Python: The Libraries You Need to Know
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Software
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News
Webcasts and Webinars
Courses
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From Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers
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Features
The Anatomy of Deep Learning Frameworks
- Data Science vs Fake News Contest
- An Overview of Python Deep Learning Frameworks
- Moving from R to Python: The Libraries You Need to Know
Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers
- 17 More Must-Know Data Science Interview Questions and Answers, Part 2
- Learn about modeling methods at PAW Chicago, Jun 19-22
Software
Tutorials, Overviews, How-Tos
- What I Learned Implementing a Classifier from Scratch in Python
- What is a Support Vector Machine, and Why Would I Use it?
- Introduction to Correlation
- The Gentlest Introduction to Tensorflow - Part 4
Opinions
- The Human Data Scientist: Safeguarding Your Career in the World of Automation
- The Top 5 KPIs to Consider When Measuring Your Campaign
- Machine 4.0: Making your Factory, Production and Maintenance Data Work
- Machine Learning-driven Firewall
News
Webcasts and Webinars
- Continuum Analytics Webinar, Mar 8: Deliver New Business Value with Open Data Science
- Stanford Webinar, Mar 9: When big data seems too small
Courses
Meetings
- KDnuggets Exclusive: Analytics and Data Science leaders in San Francisco, Apr 3-5
- Learn about modeling methods at PAW Chicago, Jun 19-22
Jobs
- Altria: Applied Statistician, Regulatory Affairs
- Netflix: Manager, Content Programming Science & Algorithms
- Aetna: Principal Data Scientist
- ACI Worldwide: Sr. Data Scientist
- R2: DevOps for Data Science
Top Tweets
Image of the Week
From Gartner 2017 Magic Quadrant for Data Science Platforms: gainers and losers