Reading List
Deep Learning
- Want an open-source deep learning framework? Take your pick
- Caffe
- DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe
- Brewing ImageNet
Data Science Background
- A Few Useful Things to Know about Machine Learning by Pedro Domingos (another post)
- Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part One)
- Everything You Wanted to Know About Machine Learning, But Were Too Afraid To Ask (Part Two)
- When Regularization Fails
Data Manupulating
Data Visualization
- Data Visualization with ggplot2 Cheat Sheet
- Data Visualization with ggplot
- Five Interactive R Visualizations with D3, ggplot2, & RStudio
- Data visualisation in R with ggplot2 and plyr
Feature Engineering
- Discover Feature Engineering, How to Engineer Features and How to Get Good at It
- Feature Engineering: How to transform variables and create new ones?
- Feature Engineering Tips for Data Scientists
- Introduction to Feature Engineering
Data Modelling
Model Evaluation
- An introduction to ROC analysis
- Illustrated Guide to ROC and AUC
- Evaluation and Cost-Sensitive Learning
Kaggle
- Learning from the best
- Profiling Top Kagglers: Gilberto Titericz, New #1 in the World
- Profiling Top Kagglers: Owen Zhang, Currently #1 in the World
Interview Questions
- Leaving Academia: How To Get A Job In Industry After Your PhD
- 50 Questions to Test True Data Science Knowledge