TLDRai.com Too Long; Didn't Read AI TLDWai.com Too Long; Didn't Watch AI
Download
Make unlimited summaries with AI!
Upgrade to PRO US$ 7.0/m
No restricted functions

Introduction to Machine Learning: Module 6.9 GMM-EM hyper-parameter tuning

https://www.youtube.com/watch?v=oTShMHt3psA&t=2s
The speaker is discussing the use of Bayesian Information Criterion (BIC) in Gaussian Mixture Model Expectation-Maximization (GMM-EM) clustering. The BIC measures how well a model fits the data, and it helps determine the number of clusters in a dataset. GMM-EM can capture differences in probability among clusters, which can impact the choice of the number of clusters. In contrast, K-means assumes equally likely clusters, while GMM-EM can encode probabilities of belonging to different clusters. The lecture highlights similarities and differences between these two clustering algorithms.
PRO users get Higher Quality summaries
Upgrade to PRO US$ 7.0/m
No restricted functions
Summarize local video Summarize online video

Get better quality outputs with more features

Become PRO
Machine Learning Module GMM-EM hyper-parameter hyper-parameter tuning Learning Machine Introduction GMM-EM tuning hyper-parameter

Related summaries

Download
Summarize local video Summarize online video

Get better quality outputs with more features

Become PRO




Total summary of videos made on TLDWai.com:

2,629

Privacy policy Terms of service Contact us Developers

We hope you enjoy our AI tool. Our project is developed with Django.
© 2024 TLDWai.com| VPS.org LLC | Made by Lou