MACHINE LEARNING PEAK OF INFLATED EXPECTATIONS – ISSUE 03: COGNITIONX DATA SCIENCE, AI AND MACHINE LEARNING WEEKLY ROUNDUP

 


Gartner’s Hype Cycle specifically focuses on the set of technologies that is showing promise in delivering a high degree of competitive advantage over the next five to 10 years. The 2016 report shows Machine Learning at the “Peak of Inflated Expectations.”

I’m looking forward to reading the blogs and articles off the back of this research. Who agrees? disagrees? Is Gartner’s analysis helpful? Should you make a decision based on their research? Let me know and we will publish your opinions.

Tabitha ‘SmartDust’ Goldstaub

Data Visualisation

Gartner has machine learning at the peak of its hype cycle

Analyst group Gartner just released their new edition of their Hype Cycle Chart and ML will be “the most disruptive class of technologies over the next ten years”.

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Popularity of deep learning frameworks

This chart nicely visualizes the 18 deep learning frameworks, ranked by popularity. It is no wonder that TensorFlow is the number one.

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Chat Bots, yadda yadda yadda

The Bot Landscape

170 bot companies, that received $4 billion in funding. It’s hard to keep track of this army of bots, but luckily Venturebeat provides us with a full landscape.

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Something to get involved in

What would you do with open data from the Olympics?

The ODF not only offers real life data, but also data about participants and sporting rules. Once you have access to the ODF, you can analyse the most current data as well as from past Olympic Games. Hence, the ODF is the key when it comes to Olympic data analysis and unsurprisingly it is not free to access for everyone. In contrary, the platform works as service for paying partners, such as media outlets that want to cover the Olympics through their own channels. Still, if you’re interested in how exactly the system works, it’s recommended to have a look at the website since the documentation is openly accessible.

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Education, training and advice we rate

Practicing Machine Learning techniques in R with MLR package

Working in R, you have to make use of multiple packages for doing different machine learning tasks. MLR is an R package that includes all of the ML algorithms that are used frequently.

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Ethics question for the day

Book review: Weapons of MATH destruction

Weapons of Math Destruction is the Big Data story Silicon Valley proponents won’t tell. The author, Cathy O’Neil, is a former academic mathematician and ex-hedge fund quant at D.E. Shaw, once part-owned by Lehman Brothers.

Her book pithily exposes flaws in how information is used to assess everything from creditworthiness to policing tactics, with results that cause damage both financially and to the fabric of society.

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Coding experiments

Machine Learning meets ketosis: how to effectively lose weight

How a machine learning model helped the author to reach his ideal weight, utilizing a high-fat diet.

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Building an autonomous RC-car using machine learning

Check out this autonomous RC car based on the Raspberry PI using Python.

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Inspiration

Use Big Data to create value for customers not just target them

Big Data has helped businesses to predict customer’s next transaction, but winning the next transaction might only lead to a short-term tactical advantage. This article published by Harvard Business Review explains how to create value for customers in order to create long-term value for the business.

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Pure unadulterated research

Choice of Instagram filters is a more accurate indicator of mental health than the average doctor’s diagnosis

According to this study, conducted by the University of Harvard and the University of Vermont, our choice of Instagram filters effectively predicts our mental health. The study made use of algorithmic face detection, colour analysis and metadata components.

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