Deep Learning Checklist
Recently I started the Deep Learning Specialization on Coursera. While I studied neural networks in my masters program (from Andrew Ng himself!), that was a long time ago and the field has changed considerably since then.
Repeatability
As businesses continue to invest in data-driven decision making, it becomes increasingly important to ensure the methods underlying those decisions are reliable. Unfortunately, we cannot take this for granted! Read on to learn a collection of best practices to make sure your decision making process rests on a sturdy foundation.
Optimal Experiment Design
We can plan sample sizes to control the width of confidence intervals.
Three Goals of Statistics: Description, Prediction, and Prescription
The great successes of Machine Learning in recent years are based on our ability to extrapolate and predict based on data. The next big step is learning and leveraging the relationship between cause and effect to prescribe what action to take.
Rotations, Orientations, and their Representations
Orientations pose an interesting challenge in polymorphism. Let’s implement a library in Rust!
Confidence Intervals
Statistical analysis is not complete without an estimate of residual uncertainty.
Rotational Axis Theorem (JIM)
The Rotational Axis Theorem allows us to decompose the dynamics of complicated systems into simpler components.
Statistical Power
Power considerations drive the sample sizes needed for a successful experiment.
Counterfactuals and Causal Reasoning
What does ‘Why?’ mean anyway?
Fisher's Exact Test
Simulation-based inference sits on a rigorous foundation.
