Intro to Machine Learning: Gradient Descent (With Code)

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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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📚 *Chapters*
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)
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