#ML Weekly 20150907 – The Best New Machine Learning Content of the Week

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Here is a roundup of Machine Learning content from this past week ordered by their social sharing volume.  I manually gather the most shared Machine Learning content in Twitter, Facebook, Google+ and LinkedIn.

“15 Players that Use Machine Learning in FinTech Space – Let’s Talk Payments”
Machine learning is a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Emerging FinTech companies are using machine learning to adapt the changes in real time.

Publish date: 9/1/2015
Social sharing volume in the past week:
131 897 259 5

“Machine learning: Mainstream tools for your business | ZDNet”
Machine learning technologies have become mainstream tools, building on the compute capabilities of cloud services and the API-based service development model. We examine what the leading players have to offer.

Publish date: 9/1/2015
Social sharing volume in the past week:
333 77 148 6

“Auction site Trade Me trials machine learning in the cloud | ZDNet”
Microsoft’s Azure ML fits the bill to predict and improve online auction outcomes.

Publish date: 9/1/2015
Social sharing volume in the past week:
344 46 38 0

“Machine Learning Method: Semi-Supervised Clustering”
Clustering is a canonical example of un-supervised machine learning methods. Un-supervised, as in, true clusters (segments) don’t exist or aren’t known in advance. Hence method tries to separate observations in different groups without any way to verify if model has done good job or not. There are various ways we can try to measure performance of un-supervised clustering: Within-Cluster-Sum-of-Squares is one, Silhouette Coefficient is another. We talked about both in our previous blog post on selecting right number of clusters for k-Means clustering algorithm.

Publish date: 9/2/2015
Social sharing volume in the past week:
57 58 30 1

“FeatureFu: A Machine Learning Toolkit Released as Open Source by LinkedIn”
The idea of machine learning is not new, but it’s also fairly unrecognized by developers. Its goal is to study machine operations and operations to create methods allowing computers to teach themselves how to write their own code for better features or quicker optimization.

Publish date: 9/4/2015
Social sharing volume in the past week:
99 29 18 2

“Twitter Is Using Machine Learning to Improve Its Machine Learning”
One the of challenges with machine learning, according to Whetlab CEO Ryan Adams, is that “it often feels like there’s a lot of black magic involved”—lots of levers and slider and buttons and knobs that take an army of PhDs to configure.

Publish date: 9/3/2015
Social sharing volume in the past week:
101 270 17 9

“Technology that uses machine learning to quickly generate predictive models from massive datasets”
Fujitsu Laboratories today announced the development of a machine-learning technology that can generate highly accurate predictive models from datasets of more than 50 million records in a matter of hours.

Publish date: 9/7/2015
Social sharing volume in the past week:
48 14 11 6
“Machine Learning Checklist”
How do you get accurate results using machine learning on problem after problem?

Publish date: 9/2/2015
Social sharing volume in the past week:
38 10 67 5

“FocusMotion Launches Fitness SDK – Machine Learning for Human Movement – insideBIGDATA”
FocusMotion, a technology leader in making it easy for developers to automatically track, identify, and count human movements with wearable device, has announced it is making its machine learning-based SDK for wearable fitness tracking public for download. The company’s hardware and OS agnostic SDK unlocks new consumer insights and behaviors. FocusMotion does all of the complex signal processing, algorithm, and device integration work so developers can focus on making engaging applications. FocusMotion technology is like Apple’s Siri for human movement.

Publish date: 9/3/2015
Social sharing volume in the past week:
42 7 6 1

“Machine Learning for Critical Workflows | Part III | Cray Blog”
This is the third and last blog entry in a series of three. The first one introduced big data analytics and critical workflows. The second post discussed critical workflows in oil and gas and in life sciences. This one will speculate about machine learning techniques to optimize such workflows.

Publish date: 9/2/2015
Social sharing volume in the past week:
3 5 31 3

“Machine Learning Enhances Recommendations – DATAVERSITY”

Publish date: 9/3/2015
Social sharing volume in the past week:
25 3 7 0

“This RC car was taught how to drift using machine learning – htxt.africa”
Machine learning is an amazing new branch of science that focuses on coaxing computers to act without being specifically programmed to do so. The general formula to accomplish this is to give a computer a clear end goal, and then let it fail over and over again until it learns from those mistakes and finally achieves that goal. In other words, the machine learns.

Publish date: 9/4/2015
Social sharing volume in the past week:
9 3 2 2

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