Gradient Descent is one of the first algorithms you learn in machine learning (a subset of AI artificial intelligence). It is one of the most popular optimization algorithms for training a machine learning model. This iterative, first-order algorithm is used to find the local minima (or maxima) of a function. In machine learning, we use this algorithm to minimize a cost or loss function, usually in a linear regression. This video contains an explanation with math, as well as code for the algorithm.

View the code here –

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0:00 – Intro

0:00 – What does a machine actually learn?

01:25 – Loss cost functions (MSE)

01:45 – Real world example

02:25 – The gradient descent algorithm

03:25 – Checking for convexity

04:20 – Obtaining the gradient of a function

05:15 – Gradient descent formula

05:40 – Gradient descent in simple terms

06:05 – Coding gradient descent in Python

7:00 – Gradient descent function

9:50 – Function functions lol

10:50 – Results

11:40 – Plotting results

14:12 – Quasi-convex function example

16:14 – Outro

#programming #ai #math

By: Daniel K.

Title: Intro to Machine Learning: Gradient Descent (With Code)

Sourced From: www.youtube.com/watch?v=DJ6VqrXfuOA

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