overfitting
3 free lessons tagged overfitting across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Validation: Measuring Anything at All
Shuffled cross-validation reports 54 percent accuracy on data containing no signal, because neighbouring samples share their futures. This lesson builds purging, embargo and sample uniqueness weights, shows the illusion scaling with label overlap and vanishing when the fix is applied, and covers why a single walk-forward path is weak evidence.
Why Standard Machine Learning Fails on Market Data
Machine learning practice assumes independent samples, a stable data-generating process, and an environment that does not react to you. Financial data violates all three, and the result is models that validate beautifully and lose money. This lesson establishes what is actually broken before the rest of the course fixes it.
Training: Optimization and Regularization
Go from a raw neural network to one that actually generalizes. Covers loss functions (MSE, cross-entropy), gradient descent variants (SGD, momentum, Adam), learning-rate effects, overfitting vs underfitting, and the regularization toolkit (L2/dropout/early stopping/batch norm).