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57:55EN
Lex FridmanPódcastFrom Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement LearningPresenta: Lex FridmanLex Fridman's MIT 6.S094 lecture walks from the AI pipeline and Q-learning to deep Q-networks, AlphaGo Zero and the DeepTraffic competition.
1:13:22ENLex FridmanPódcastThe Second 90%: What It Really Takes to Put Deep Learning on a Driverless CarPresenta: Lex FridmanWaymo's Sacha Arnoud walks MIT students through Waymo's history, its perception stack, and the industrial-scale labeling, compute and testing behind it.
57:15ENLex FridmanPódcastInside Torch and Autograd: How Deep Learning Libraries Compute GradientsPresenta: Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:20:58ENLex FridmanPódcastFrom Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email RepliesPresenta: Lex FridmanA Deep Learning School talk that builds sequence-to-sequence models step by step around one running example, automatic email reply, and then surveys memory and operation-augmented networks.
1:19:48ENLex FridmanPódcastBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep LearningPresenta: Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.
1:02:06ENLex FridmanPódcastLearning TensorFlow Hands-On: From a Mystery Line to Handwritten DigitsPresenta: Lex FridmanGoogle Brain's Sherry Moore walks a Deep Learning School audience through how TensorFlow works and two core labs, linear regression and MNIST digit recognition.
53:13ENLex FridmanPódcastHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene SegmentationPresenta: Lex FridmanLex Fridman's MIT 6.S094 lecture walks from the basics of image classification through landmark network architectures to pixel-level segmentation, optical flow and the SegFuse competition.
34:33ENLex FridmanPódcastTeaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous VehiclesPresenta: Lex FridmanLex Fridman's MIT 6.S094 lecture on using driver-facing cameras and neural networks to understand body pose, gaze, emotion and cognitive load.
52:28ENLex FridmanPódcast · 2 vocesRay Kurzweil on Hierarchies, the Neocortex and Merging with AIInvitado: Ray Kurzweil · Presenta: Lex FridmanIn an MIT AGI course lecture, Kurzweil connects the history of neural networks to his hierarchical theory of the neocortex, language understanding at Google, longevity, jobs and existential risk.
1:00:15ENLex FridmanPódcast · 2 vocesCircuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is GoingInvitado: Ilya Sutskever · Presenta: Lex FridmanIn an MIT AGI lecture, OpenAI's co-founder explains why deep learning works, then walks through meta-learning, hindsight experience replay, sim-to-real transfer, self-play and learning goals from human feedback.
1:31:29ENLex FridmanPódcastIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving CarsPresenta: Lex FridmanThe first lecture of MIT 6.S094 introduces neural networks, reinforcement learning and computer vision through the question of how hard driving really is.
1:12:00ENLex FridmanPódcastWhy Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised LearningPresenta: 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.
1:02:24ENLex FridmanPódcastFrom Looping Forklifts to Self-Driving Cities: Sertac Karaman on Motion PlanningPresenta: Lex FridmanAn MIT professor traces motion planning from the DARPA Urban Challenge to the future of transportation and the cities built around cars.
1:27:41ENLex FridmanPódcastDeep Learning in 2020: Lex Fridman's Tour of Transformers, Self-Play, Self-Driving and What Comes NextPresenta: 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.
51:18ENLex FridmanPódcast · 2 vocesEngineering Intelligence: Lex Fridman's Opening Lecture for MIT's AGI CourseInvitado: MIT AGI · Presenta: 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.
1:02:06ENLex FridmanPódcastDeep Learning for Self-Driving Cars: Why Edge Cases and Human Nature Are the Real ProblemPresenta: 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.