Subject
5 entries
Practical
Bookmarks
Disaster Planning for Regular Folks
lcamtuf's level-headed disaster prep guide — practical, non-paranoid preparation for disruptions like power outages, earthquakes, or civil unrest. Written by a security researcher who applies the same adversarial thinking to personal resilience.
What I Wish I Knew About Data For Startups
Jean-Nicholas Hould's hard-won lessons about building data capabilities at startups — prioritizing tracking over models, avoiding premature data infrastructure, and why startups fail at data for different reasons than large companies.
Approaching (Almost) Any Machine Learning Problem
Abhishek Thakur's systematic framework for tackling any supervised ML problem — from data cleaning and feature engineering through model selection and stacking. One of the most-shared practical ML workflow guides from the Kaggle blog era.
3 Things About Data Science You Won't Find In Books
Three things about data science that don't appear in textbooks — the communication overhead, the 80% time spent on data wrangling, and the organizational politics of getting models deployed. Classic practitioner wisdom from Galvanize/KDnuggets circa 2015.
10 Tips for Better Deep Learning Models
Laura Diane Hamilton's ten practical tips for improving deep learning model performance, covering data preparation, architecture choices, regularization, and training tricks. A snapshot of practitioner wisdom circa 2014, before the era of giant pretrained models made many of these tradeoffs less urgent.