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1:12:00EN
Lex Fridman팟캐스트Why Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised Learning진행자 Lex FridmanIn a 2016 Deep Learning School talk, Yoshua Bengio explains the assumptions that let neural nets beat the curse of dimensionality and lays out the open challenges ahead.모델과 학습머신러닝 기초Deep Learning+2
1:02:24EN
Lex Fridman팟캐스트From Looping Forklifts to Self-Driving Cities: Sertac Karaman on Motion Planning진행자 Lex FridmanAn MIT professor traces motion planning from the DARPA Urban Challenge to the future of transportation and the cities built around cars.Autonomous DrivingRoboticsAI research+2
1:27:41EN
Lex Fridman팟캐스트Deep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes Next진행자 Lex FridmanAn MIT lecture surveys the biggest advances of 2017 to 2019 across language, reinforcement learning, deep learning theory and autonomous vehicles, and lists Fridman's hopes for the year ahead.모델과 학습Deep LearningAI research+3
51:18EN
Lex Fridman팟캐스트 · 2명Engineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI Course게스트 MIT AGI · 진행자 Lex FridmanLex Fridman lays out an engineering-first approach to artificial general intelligence, previews the course's projects and guest speakers, and frames deep learning's limits.모델과 학습AGI(범용 인공지능)AI research+3
1:02:06EN
Lex Fridman팟캐스트Deep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real Problem진행자 Lex FridmanLex Fridman opens MIT's 6.S094 with a tour of what deep learning is, how it works, what it has achieved and where it still breaks, framed around autonomous vehicles.모델과 학습Autonomous DrivingDeep Learning+3