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Gradient Descent Algorithm
Gradient Descent Algorithm Table Of Contents: What Is Gradient Descent? Algorithm Requirements. What Is Gradient? How Gradient Descent Algorithm Works? (1) What Is Gradient Descent? Gradient descent (GD) is an iterative first-order optimisation algorithm, used to find a local minimum/maximum of a given function. This method is commonly used in machine learning (ML) and deep learning (DL) to minimise a cost/loss function (e.g. in a linear regression). Gradient Descent algorithm is the backbone of Machine Learning because whatever the loss function you give, it will find out its local minimum value. (2) Algorithm Requirements. The Gradient Descent algorithm does not work for all functions. There
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Linear Regression: OLS Technique
Linear Regression – OLS Technique. (1) Introduction. The Linear Regression model’s main objective is to find the best-fit line that will closely pass through all the points to minimize the loss. The question here is how to find out the best-fit line. We need some mathematical proof that this is my best-fit line. To solve this problem we have two techniques. Ordinary Least Square Technique. Gradient Descent Technique. (2) Ordinary Least Square Technique. In case of OLS technique we directly use the formula for ‘m’ and ‘b’ to derive the best fit line equation. Here in this vlog we will
