Machine Learning

Gradient Descent

2023.05.31

Gradient Derivatives We can also interpret the slope as: If I nudge x, how does y change? Pretend that multivariable funciton is univariate, then take derivative as normal. This is a partial derivative. The gradient extends the derivative to multiple variables. Vector-valued function (always outputs a vector). The gradient of f(θ) w.r.t. θ is a vector. Each element is the partial derivative for ..

Computer Science 🌋/Machine Learning🐼

Foundation of Machine Learning

2023.05.31

Modeling Making Predition To make a prediction, we choose a model, Constant Model: Prediction: fθ(x) = θ (Recipe to compute the prediction) Simple Linear Model: fθ(x) = θ1x + θ0 ( Two model weights) The Constant Model Start simple: if constant model, how do we pick θ? Intuition: pick θ to be close to most of the values in data Model Loss Use x to denote what we use to make predictions Use y to d..

Gradient Descent

Foundation of Machine Learning

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