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Weekly Selection – June 28, 2019

Five Command Line Tools for Data Science

By Rebecca Vickery – 6 min read

You can do more data science than you think from the terminal


Beating State of the Art by Tuning Baselines

By Rachael Tatman – 5 min read

How do you know if a new machine learning model is an improvement over previous models?


7 Tips for Dealing With Small Data

By Daniel Rothmann – 7 min read

Because more often than not, that’s what you’re gonna get.


Stand Up for Best Practices

By Rajiv Shah – 5 min read

Misuse of Deep Learning in Nature’s Earthquake Aftershock Paper


Deep Dive into Catboost Functionalities for Model Interpretation

By Alvira Swalin – 8 min read

Do we really understand what happens inside ML models we build? Let’s explore.


Tips, Tricks, Hacks, and Magic: How to Effortlessly Optimize Your Jupyter Notebook

By Anne Bonner – 13 min read

The complete beginner’s guide to making Jupyter Notebooks better, faster, stronger, smoother, and just plain awesome


Can Machine Learning Read Chest X-rays like Radiologists?

By David W. Dai – 8 min read

Using adversarial networks to achieve human-level performance for chest x-ray organ segmentation


Getting started with Gradient Boosting Machines – using XGBoost and LightGBM parameters

By Nityesh Agarwal – 10 min read

If you want to use GBMs for modelling your data, I believe that, you have to get atleast a high-level understanding of what happens on the inside.


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