Hyperparameter Optimization - The Math of Intelligence #7

Hyperparameters are the magic numbers of machine learning. We're going to learn how to find them in a more intelligent way than just trial-and-error. We'll go over grid search, random search, and Bayesian Optimization. I'll also cover the difference between Bayesian and Frequentist probability.

Code for this video: https://github.com/llSourcell/hyperparameter_optimization_strategies

Noah's Winning Code:
https://github.com/NoahLidell/math-of-intelligence/tree/master/probability_theory

Hammad's Runner-up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Naive%20Bayes%20Classifier

More learning resources:
https://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf
https://thuijskens.github.io/2016/12/29/bayesian-optimisation/
https://jmhessel.github.io/Bayesian-Optimization/
https://arimo.com/data-science/2016/bayesian-optimization-hyperparameter-tuning/
https://dhnzl.files.wordpress.com/2016/12/fuzzymad2016_bo_pdf.pdf
http://blog.revolutionanalytics.com/2016/06/bayesian-optimization-of-machine-learning-models.html
https://www.youtube.com/watch?v=cWQDeB9WqvU
https://nlpers.blogspot.nl/2014/10/hyperparameter-search-bayesian.html
http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html

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https://www.patreon.com/user?u=3191693

Thanks to Veritasium (bayesian animation) & Angela Schoellig (drone clip)
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