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w := w - α · ∇L(w) ŷ = X · w + b
Machine Learning★ Featured
26m
SCHEMATIC // OPTIMIZATION_PIPELINESPEC v2.4
STAGE 01Feature Tensor X
STAGE 02Hypothesis h_θ(x)
STAGE 03Loss Function J(θ)
STAGE 04AdamW ∇J Step
KERNEL:w_{t+1} = w_t - η / √(v̂_t + ε) · m̂_t
VERIFIED BLUEPRINTSYS // ML-REV2
26 min read

Regression: Simple Linear Regression

Master simple linear regression from first principles. Learn closed-form Ordinary Least Squares (OLS), cost functions, slope & intercept derivations, and Python implementations.

A
Atul Jha
03 Jan 2026
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