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슬라이드 4장1:03:21EN
JSJane Street· 2024년 12월 9일Horace He on Building ML Systems for Trillion-Trillion OperationsHorace He · MetaHow GPU bottlenecks, compiler design and distributed parallelism shape the systems behind large-scale model training.GPU programming & kernelsDistributed training & inferenceMachine Learning+2
3:16EN
JSJane Street· 2024년 7월 18일JP on Machine Learning and Trading at Jane StreetJP · Jane StreetA Hong Kong equities trader describes his changing workday, the growth of research infrastructure, and the distinctive challenges of machine learning in finance.Machine LearningFintech인프라와 배포+1
2:46EN
JSJane Street· 2024년 7월 18일Matt on Machine Learning and Trading at Jane StreetMatt · Jane StreetA Hong Kong equities trader describes the challenges of financial data, the firm’s ML roles and its collaborative culture.Machine Learning금융 AICareers+1
37:00EN
Lex Fridman팟캐스트Inside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the Code진행자 Lex FridmanYouTube's head of search and discovery, Cristos Goodrow, explains to Lex Fridman how search and recommendations work, which signals matter, and how the team measures whether viewers are better off.모델과 학습Recommendation systemsSearch & retrieval+3
10:39EN
Lex Fridman팟캐스트Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them진행자 Lex FridmanBerkeley's Michael I. Jordan explains where statistics came from, why it is really about decisions, and how empirical Bayes and false discovery rate bridge two rival philosophies.모델과 학습머신러닝 기초Decision Making+2