Linear Algebra, Machine Learning & Deep Learning Articles

Algorithm complexity (Big-O) Charts

Interactive Linear Algebra (ebook)
Pandas acceleration with Modin
Scikit-learn v0.22 - new features!
Split Criteria for Decision Trees: Gini Impurity vs Entropy vs Variance (ML Whiz)
Matrix Calculus ebook (
What is UMAP? (pair-code.github)
FastBert: A Deep Learning Library for BERT Models (Medium)
A Pirate's Guide to Accuracy, Precision, Recall & other scores (Floydhub)
A Guide to Reinforcement Learning (JFPettit.github)
Machine Learning Foundations (MIT Press ebook)
Density Estimation Techniques (Analytics Vidhya)
Inference Hardware Benchmarks (Next Platform)
Intro to Policy Gradients (GitHub)
Multi-Armed Bandits - and A/B Testing (R-Bloggers)

Epsilon Greedy, Upper Confidence Bounds, Thompson Sampling
150 Machine Learning models at - lessons learned (
  • Projects using ML models ==> strong business value.
  • Model performance != business performance.
  • Be clear about the problem you’re trying to solve.
  • Prediction serving latency matters.
  • Get early feedback on model quality.
  • PyTorch Lightning (PiPy)

    William Falcon's YouTube talk