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Weekly Selection – Apr 20, 2018

Interpretable Machine Learning with XGBoost


by Scott Lundberg – 10 min read

This is a story about the danger of interpreting your machine learning model incorrectly, and the value of interpreting it correctly. If you have found the robust accuracy of ensemble tree models such as gradient boosting machines or random forests attractive, but also need to interpret them, then I hope you find this informative and helpful.


The fall of RNN / LSTM

by Eugenio Culurciello – 8 min read

We fell for Recurrent neural networks (RNN), Long-short term memory (LSTM), and all their variants. Now it is time to drop them!


Introduction to Bayesian Linear Regression

by William Koehrsen – 10 min read

The Bayesian vs Frequentist debate is one of those academic arguments that I find more interesting to watch than engage in. Rather than enthusiastically jump in on one side, I think it’s more productive to learn both methods of statistical inference and apply them where appropriate.


What a Disentangled Net We Weave: Representation Learning in VAEs (Pt. 1)

by Cody Marie Wild – 15 min read

It’s a truth universally acknowledged: that data not in possession of labels must be in want of unsupervised learning. Glibness aside, it’s commonly understood that supervised learning has meaningful downsides: labels are costly, noisy, and direct your problem towards the achievement of a somewhat artificial goal, rather than simply learning meaningful contours of the data in a more neutral way.


Hype & Disadvantages of Neural Networks

by Niklas Donges – 8 min read

Deep Learning enjoys a massive hype at the moment. People want to use Neural Networks everywhere, but are they always the right choice? That will be discussed in the following sections, along with why Deep Learning is so popular right now.


4 Ways to fail a Data scientist job interview

by Ganes Kesari B – 6 min read

‘Data Scientist’ might well be the sexiest job of the century. But hiring one is anything but that. Actually, it can be excruciatingly painful for companies.


My Journey from Physics into Data Science

by Admond Lee – 10 min read

I still learn new knowledge everyday with my growing passion in Data Science field. To pursue different career track as a graduating physics student there must be ‘Why’ and ‘How’ questions to be answered.


Python for Finance: Stock Portfolio Analyses

by Kevin Boller – 26 min read

My two most recent blog posts were about Scaling Analytical Insights with Python; part 1 can be found [here](https://kdboller.github.io/2017/10/11/scaling-analytical-insights-with-python_part2.html) and part 2 can be found here. It has been several months since I wrote those, largely due to the fact that I relocated my family to Seattle to join Amazon in November; I’ve spent most of the time on my primary project determining our global rollout plan and related business intelligence roadmap.


The 5 Stages of a System Breakdown on NJ Transit

by Pranav Badami – 8 min read

On Friday, March 2nd, 2018, New York City was on the tail end of the first of four Nor’easters to sweep through the region in March. A wintry mix had been steadily falling from the previous night through the early afternoon, accompanied by gusty winds in the late morning hours.


Become a Better Data Scientist by Contributing to Open Source

by Lauren Oldja – 7 min read

Let’s face it: getting a great score on a Kaggle competition doesn’t require adherence to PEP8 or really any other software development best practices. And yet, code is our craft, and at some point in your career you may want or need to learn to write production-level code.


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