If you are interested in our coaching or consulting services - https://compu-flair.com/solution

To get the source code - https://compu-flair.com

In this video, Dr. Ardavan (Ahmad) Borzou will describe the concept of entropy and relative entropy in physics, statistics, and information theory and how it can be used to use data to estimate machine learning unknown parameters. 
After discussing the concepts, at the end of the video, he will show how to use Python and its libraries to run the implementations of the presented methods to uncover linear regression parameters from a sample data spreadsheet. 

Chapters:
00:00 - Introduction
00:41 - Summary of Past Videos
02:28 - Surprise, Action, Free Energy
02:55 - Uncertainty VS Randomness
04:28 - Free Energy Minimum 
04:51 - Surprise
05:43 - Entropy
08:26 - Probability Estimation
08:54 - Relative Entropy
10:17 - Add Data
11:42 - Minimal Information Loss
12:20 - Minimization to Maximization Trick
13:11 - Likelihood
14:35 - Analytic Solution to Linear Regression
16:00 - Residual Sum of Squares
17:45 - Implementation in Python
19:36 - Source Code & AI Assistant
20:38 - Coaching or Consulting Services

Debajyati Dey

Data Science

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The concept of relative entropy, introduced by Ludwick Bolman and reintroduced by Colback and Liv Layer, is fundamental in estimating machine learning model parameters. This post explains the basic concepts, the role of the maximum entropy principle, and how to derive and estimate linear regression parameters using Python. The author also touches on the differences between uncertainty and randomness, and highlights practical implementations using Python libraries like Pandas and Scikit-learn.

This Physics Formula Unlocks Machine Learning Parameters: A Complete Picture

<p>Very good explanation for people with the background in physics. Thank you for your work!</p>
