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Weekly Selection – May 25, 2018

The Need and Importance of Model Interpretation

By Dipanjan (DJ) Sarkar – 12 min read

The field of Machine Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more.


Using Unsupervised Learning to plan a vacation to Paris: Geo-location clustering

By Hamza Bendemra, Ph.D. – 6 min read

When the Girlfriend told me she was planning a 10-day trip to Paris with one of her girlfriends, I figured I could help.


How to build Animated Charts like Hans Rosling – doing it all in R

By Tristan Ganry – 5 min read

Hans Rosling was a statistics guru. He spent his entire life promoting the use of data with animated charts to explore development issues and to share a fact-based view of the world.


The 10 Mining Techniques Data Scientists Need for Their Toolbox

By James Le – 30 min read

At their core, data scientists have a math and statistics background. Out of this math background, they’re creating advanced analytics. On the extreme end of this applied math, they’re creating machine learning models and artificial intelligence.


Data Science for Startups: Business Intelligence

By Ben Weber – 17 min read

A lot of the heavy lifting involved in setting up data science at a startup is convincing the product team to instrument and care about data. If you’re able to achieve this goal, the next step is being able to answer all sorts of questions about product health within your organization.


Reinforcement Learning for Real Life Planning Problems

By Philip Osborne – 15 min read

Recently, I have published some examples where I have created Reinforcement Learning models for some real life problems. For example, using Reinforcement Learning for Meal Planning based on a Set Budget and Personal Preferences.


Time Series Nested Cross-Validation

By Courtney Cochrane – 18 min read

This blog post discusses the pitfalls of using traditional cross-validation with time series data. Specifically, we address 1) splitting a time series without causing data leakage, 2) using nested cross-validation to obtain an unbiased estimate of error on an independent test set, and 3) cross-validation with datasets that contain multiple time series.


Into a Textual Heart of Darkness

By Leon Zhou – 8 min read

The internet is a jungle. Here, rich diversity and wondrous color combine to create a unique ecosystem, enabling novel technologies and methods of communication.


Did Google Duplex beat the Turing Test? Yes and No.

By Artem Oppermann – 4 min read

Even though the accomplishment of Google in the area of voice AI is groundbreaking there are two reasons (In my opinion) why Google AI did not pass the Turing Test. However there are still good news.


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