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.