Power K-Means
Data Skeptic7 Maalis 2022

Power K-Means

In today's episode, Jason, an Assistant Professor of Statistical Science at Duke University talks about his research on K power means. K power means is a newly-developed algorithm by Jason and his team, that aims to solve the problem of local minima in classical K-means, without demanding heavy computational resources. Listen to find out the outcome of Jason's study.

Click here to access additional show notes on our website!

Thanks to our Sponsors:
ClearML is an open-source MLOps solution users love to customize, helping you easily Track, Orchestrate, and Automate ML workflows at scale. https://clear.ml

Springboard
Springboard offers end-to-end online data career programs that encompass data science, data analytics, data engineering, and machine learning engineering.

Tämä jakso on lisätty Podme-palveluun avoimen RSS-syötteen kautta eikä se ole Podmen omaa tuotantoa. Siksi jakso saattaa sisältää mainontaa.

Jaksot(601)

Video Recommendations in Industry

Video Recommendations in Industry

In this episode, Kyle Polich sits down with Cory Zechmann, a content curator working in streaming television with 16 years of experience running the music blog "Silence Nogood." They explore the inter...

26 Joulu 202538min

Eye Tracking in Recommender Systems

Eye Tracking in Recommender Systems

In this episode, Santiago de Leon takes us deep into the world of eye tracking and its revolutionary applications in recommender systems. As a researcher at the Kempelin Institute and Brno University,...

18 Joulu 202552min

Cracking the Cold Start Problem

Cracking the Cold Start Problem

In this episode of Data Skeptic, we dive deep into the technical foundations of building modern recommender systems. Unlike traditional machine learning classification problems where you can simply ap...

8 Joulu 202539min

Designing Recommender Systems for Digital Humanities

Designing Recommender Systems for Digital Humanities

In this episode of Data Skeptic, we explore the fascinating intersection of recommender systems and digital humanities with guest Florian Atzenhofer-Baumgartner, a PhD student at Graz University of Te...

23 Marras 202536min

DataRec Library for Reproducible in Recommend Systems

DataRec Library for Reproducible in Recommend Systems

In this episode of Data Skeptic's Recommender Systems series, host Kyle Polich explores DataRec, a new Python library designed to bring reproducibility and standardization to recommender systems resea...

13 Marras 202532min

Shilling Attacks on Recommender Systems

Shilling Attacks on Recommender Systems

In this episode of Data Skeptic's Recommender Systems series, Kyle sits down with Aditya Chichani, a senior machine learning engineer at Walmart, to explore the darker side of recommendation algorithm...

5 Marras 202534min

Music Playlist Recommendations

Music Playlist Recommendations

In this episode, Rebecca Salganik, a PhD student at the University of Rochester with a background in vocal performance and composition, discusses her research on fairness in music recommendation syste...

29 Loka 202552min

Bypassing the Popularity Bias

Bypassing the Popularity Bias

15 Loka 202534min

Suosittua kategoriassa Tiede

rss-mita-tulisi-tietaa
rss-poliisin-mieli
tiedekulma-podcast
menologeja-tutkimusmatka-vaihdevuosiin
sotataidon-ytimessa
filocast-filosofian-perusteet
rss-duodecim-lehti
rss-astetta-parempi-elama-podcast
rss-lapsuuden-rakentajat-podcast
utelias-mieli
docemilia
radio-antro
rss-ranskaa-raakana
rss-kasvatuspsykologiaa-kaikille
rss-tiedetta-vai-tarinaa
rss-luontopodi-samuel-glassar-tutkii-luonnon-ihmeita
rss-sosiopodi