Hyperparameter Tuning In Machine Learning | Grid Search, Random Search, Bayesian Optimization

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In this video, we deep dive into the world of hyperparameter tuning, an essential technique for optimizing machine learning models and performing well in AI/ML interviews. We’ll cover key tuning methods like Grid Search, Random Search, and Bayesian Optimization, with clear examples using decision trees and deep learning models. You’ll learn how hyperparameter tuning improves model performance, reduces training time, and helps prevent overfitting. We also clarify the difference between model parameters and hyperparameters, and how adjusting them can lead to better results.

✅Timestamps:

00:00 - Introduction to Hyperparameter Tuning and its Importance in AI/ML Interviews
01:16 - Hyperparameter Tuning Fundamentals: Purpose & Key Concepts
02:49 - Parameter Complexity: Combinations and Grid Exploration
07:03 - Grid Search Method: Complete Enumeration vs Computational Costs
08:26 - Random Search: Efficient Alternative to Grid Search
09:51 - Bayesian Optimization: Informed Decision-Making
11:18 - RAG & Model Training: Integrating External Data for Accuracy
12:36 - Practical Guidance: Mastering Hyperparameter Tuning through Experience

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