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Library/ Machine Learning (Supervised + Unsupervised)/ Supervised Learning by Building/ S01 · Build: How Training Works (fit, loss, optimisation)

subtopic

Loss functions and what they reward

10 min applied15 min research1 curated resources

Compute log loss, MSE, MAE and Huber loss for the same predictions and compare. Concepts: Loss/objective functions; log loss; MSE vs MAE vs Huber Project A: Log loss of the dummy vs logistic regression. Project B: MSE vs MAE ranking of two forecasts that differ only at peak hours.

Builds on: B17 · Build: Fit a Line with Gradient Descent (calculus in action), B18 · Build: Spam Classifier with Probabilities (sigmoid, softmax, cross-entropy)

Notes, key code, what to skip and 1 curated resources are inside the platform.Join the waitlist and we'll build you a roadmap that includes this topic only if your goal needs it.

Loss functions and what they reward · 1x1labs.ai