Resources for developers using Python for scientific computing and quantitative analysis
under Machine Learning
Bayesian Optimization provides a principled technique based on Bayes Theorem to direct a search of a global optimization problem that is efficient and effective. It works by building a probabilistic model of the objective function, called the surrogate function, that is then searched efficiently with an acquisition function before candidate samples are chosen for evaluation on the real objective function.
under Statistics
If you think Bayes’ theorem is counter-intuitive and Bayesian statistics, which builds upon Baye’s theorem, can be very hard to understand. I am with you.
There are countless reasons why we should learn Bayesian statistics, in particular, Bayesian statistics is emerging as a powerful framework to express and understand next-generation deep neural networks.
under Data Science
under Data Science
under Machine Learning
under Machine Learning