ML Extra Material

This jupyter book contains extra material to those included on my ML introductory course and also on the Fundamentals of Deep Learning.

The aim is to organize the material of additional topics of interest. Some of the notebboks were developed by me and many others are based on open notebooks shared by others individually acknolwedged accordingly.

Check out the content pages:

• Bayesian Optimization
• Global Optimization with Gaussian Processes: Part1
• Global Optimization with Gaussian Processes: Part2
• Conditional generation via Bayesian optimization in latent space
• Three complementary/equivalent formulations of Difussion models