TLDRai.com Too Long; Didn't Read AI TLDWai.com Too Long; Didn't Watch AI
La'u mai
Faia ni aotelega e le gata ma AI!
Fa'afou ile PRO US$ 7.0/m
Leai ni galuega fa'atapula'aina

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.
E maua e tagata fa'aoga PRO ni aotelega o le Tulaga Maualuga
Fa'afou ile PRO US$ 7.0/m
Leai ni galuega fa'atapula'aina
Aotele le vitio i le lotoifale Aotele le vitio i luga ole laiga

Maua mea e sili atu ona lelei ma sili atu ona foliga

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

Aotelega fa'atatau

La'u mai
Aotele le vitio i le lotoifale Aotele le vitio i luga ole laiga

Maua mea e sili atu ona lelei ma sili atu ona foliga

Avea PRO




Aotelega o vitio na faia ile TLDWai.com:

2,635

Faiga fa'alilolilo Tulaga o auaunaga Faafesootai matou Developers

Matou te faʻamoemoe e te fiafia i la matou meafaigaluega AI. O la matou poloketi ua atiaʻe ma Django .
© 2024 TLDWai.com| VPS.org LLC | Faia e Lou