hyperparameters tuning - bayesian optimization

📚 RECOMMENDED BOOKS TO START WITH MACHINE LEARNING
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If you're new to ML, here are the 3 best books I recommend (I've personally read all of these):
1. Hands-On Machine Learning – the go-to practical ML guide: https://amzn.to/3UcGqSS
2. Mathematics for Machine Learning – deep dive into the theoretical aspects of ML: https://amzn.to/3IZgHe7
3. Designing Machine Learning Systems - practical strategies for building scalable ML solutions: https://amzn.to/4ojEqFX

These are affiliate links, so buying through them helps support the channel at no extra cost to you — thanks 🙏

Summary
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Learn how Bayesian optimization outperforms grid search and random search when maximizing resources in complex search spaces. This video visualizes how each method samples and converges toward the global maximum, revealing why Bayesian optimization is more efficient, adaptive, and resource-saving—ideal for high-cost evaluations and machine learning hyperparameter tuning.

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