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

subtopic

Optimisation: gradient descent, epochs and batches in practice

10 min applied10 min research2 curated resources

Train SGDClassifier / SGDRegressor for 30 epochs with partial_fit on mini-batches; plot loss per epoch. Concepts: Gradient descent recap; epochs; iterations; batch size; solvers Project A: SGD on scaled German features: loss curve per epoch. Project B: SGDRegressor on scaled electricity features: watch it diverge without scaling.

Builds on: B17 · Build: Fit a Line with Gradient Descent (calculus in action)

Notes, key code, what to skip and 2 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.

Optimisation: gradient descent, epochs and batches in practice · 1x1labs.ai