Jul
2016 Aug Opinions, Interviews
All (123) | Courses, Education (11) | Meetings (15) | News, Features (23) | Opinions, Interviews (27) | Software (8) | Tutorials, Overviews (32) | Webcasts & Webinars (7)
- How to Become a Data Scientist – Part 2 - Aug 30, 2016.
Check out part 2 of this excellent series of articles on becoming a data scientist, written by someone who spends their day recruiting data scientists. This installation focuses on learning.
- Rio Olympics 2016 on Twitter: Positive Sentiment (75%), Water Sports, Simone Biles Win - Aug 27, 2016.
Who were the most talked about athletes in the 2016 Rio Olympic Games? Which sport was most cited by users? What was the overall sentiment? This analysis by Expert System provides the detailed answers.
- Is “Artificial Intelligence” Dead? Long Live Deep Learning?!? - Aug 26, 2016.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology.
- Interpretability over Accuracy - Aug 25, 2016.
If researchers can’t understand a provided answer, it is not viable. They can’t write about techniques they don’t understand beyond “Here are the numbers. Look how pretty my model is.” Good research, that ain’t.
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How to Become a Data Scientist – Part 1 - Aug 22, 2016.
Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it! - Data Science Challenges - Aug 17, 2016.
This post is thoughts for a talk given at the UN Global Pulse lab in Kampala, and covers the challenges in data science.
- Does Data Scientist Mean What You Think It Means? - Aug 16, 2016.
Do we have an accurate idea of what "data scientist" actually means? Read this thought-provoking opinion on the topic.
- Central Limit Theorem for Data Science – Part 2 - Aug 16, 2016.
This post continues an explanation of Central Limit Theorem started in a previous post, with additional details... and beer.
- Artificial Intelligence: Useful Technology or the Next Frankenstein? - Aug 15, 2016.
For a refreshingly insightful and honest answer this question, just ask Facebook's founder Mark Zuckerberg.
- Tales from ICML: Insights and Takeaways - Aug 15, 2016.
The dust has settled from ICML 2016, having been held in June in NYC. Read some perspective on what was offered at the conference and relevant takeaways from a reflective attendee.
- Big Data Doesn’t Rule The Olympics - Aug 13, 2016.
Whenever there is a Big Data conversation, especially in sports, expectations have to be set correctly. Big Data isn’t perfect, but it is a lot better than the more superficial methods of making a judgment.
- Robots Need “Common Sense” AI to Work Out Our Uncertain World - Aug 12, 2016.
At the Machine Intelligence Summit in Berlin last week, Jeremy Wyatt, Professor of Robotics and Artificial Intelligence at University of Birmingham, was asked a few questions about his work in mobile robot task planning and manipulation.
- Stop Blaming Terminator for Bad AI Journalism - Aug 11, 2016.
Too often, we blame The Terminator for the public's misconceptions concerning machine learning. But do James Cameron and the Austrian Oak stand wrongfully accused?
- Is a Chief Data Officer Required for Analytics Success? - Aug 10, 2016.
In this insightful opinion piece, gain perspective on whether a Chief Data Officer is required for an organization's analytics success.
- Should We Be Rethinking Unsupervised Learning? - Aug 10, 2016.
Roland Memisevic, Assistant Professor at the University of Montreal and Chief Scientist at Twenty Billion Neurons, explores ideas on rethinking unsupervised learning, which he feels may explain what scientists have been doing wrong.
- 3 Thoughts on Why Deep Learning Works So Well - Aug 10, 2016.
While answering a posed question in his recent Quora Session, Yann LeCun also shared 3 high-level thoughts on why deep learning works so well.
- Advice for Data Science Interviews - Aug 9, 2016.
Check out an interview excerpt from Springboard’s Guide to Data Science Interviews. Determine how one can find data science interviews - and ace them!
- Choosing Tools for Data ETLs - Aug 9, 2016.
Which tool should I use for my data pipelines? Get some advice from a data scientist recently having gone through this pipeline tool selection process.
- Understanding the Bias-Variance Tradeoff: An Overview - Aug 8, 2016.
A model's ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results.
- Nigeria: Telling Internally Displaced Persons Stories Using Visual Data and Infographics - Aug 5, 2016.
Read a data-driven discussion on the plight of internally displaced persons (IDPs) in Nigeria, and see the real power of data science and data visualization.
- Making Data Science Accessible – HDFS - Aug 4, 2016.
This post explains some basic Big Data concepts and offers some insight into when HDFS can be useful, employing basic analogies to do so.
- Common Sense in Artificial Intelligence… by 2026? - Aug 4, 2016.
An insightful opinion piece on the future of common sense in AI. A recommended read by an authority in the field.
- Contest 2nd Place: Automating Data Science - Aug 3, 2016.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
- What Statistics Topics are Needed for Excelling at Data Science? - Aug 2, 2016.
Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.
- Data Science Automation: Debunking Misconceptions - Aug 2, 2016.
This opinion piece aims to clear up some proposed misconceptions surrounding data science automation.
- The Core of Data Science - Aug 1, 2016.
This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.
- Yann LeCun Quora Session Overview - Aug 1, 2016.
Here is a quick oversight, with excerpts, of the Yann LeCun Quora Session which took place on Thursday July 28, 2016.
Interpretable Machine Learning: The Free eBook