This Physics Force Solves Machine Learning’s Data Scarcity: Regularization Clearly Explained

🚀 Join the CompuFlair Community! 🚀 📈 Sign up on our website to access exclusive Data Science Roadmap pages — a step-by-step guide to mastering the essential skills for a successful career. 💪As a member, you’ll receive emails on expert-engineered ChatGPT prompts to boost your data science tasks, be notified of our private problem-solving sessions, and get early access to news and updates. 👉 https://compu-flair.com/user/register In this video, Dr. Ardavan (Ahmad) Borzou will discuss the low sample size problem in machine learning and why we need to revisit our estimated probability of the events. This will end in maximizing likelihood under a constraint. In this video, we use Physics concepts to interpret the penalty term and hyper-parameter in machine learning. After discussing the concepts, at the end of the video, we will show how to use Python and its libraries to run the implementations of the presented methods to uncover constrained linear regression parameters from a small sample data spreadsheet. Chapters: 00:00 - Introduction 01:00 - Review of Past Videos 04:23 - Small Data Problem 06:52 - General Solution Concept 09:19 - Re-Minimize Information Loss 10:50 - Penalty or Regularization Term 11:20 - Hyperparameter Interpretation 11:58 - Ridge Regression 12:20 - Lasso Regression 12:49 - Overfitting Solution 13:13 - Grid Search Cross Validation 17:20 - Bias Variance Tradeoff 19:37 - Variance Meaning 20:25 - Python Code Implementation