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18:46EN
Lex FridmanPodcastDeep Learning Plus One Idea: Ilya Sutskever on Building AGIAnime : Lex FridmanIn a May 2020 conversation, Sutskever discusses self-play, simulation, bodies and consciousness, tests of intelligence, and how an AGI might be governed and aligned.Modèles et entraînementAGIReinforcement Learning+3
1:31:52EN
Lex FridmanPodcastFrom Phonemes to CTC: How Deep Learning Rebuilt Speech RecognitionAnime : Lex FridmanBaidu's Adam Coates walks through the traditional speech pipeline, the CTC-based end-to-end engine that replaced much of it, and what it takes to scale and deploy it.Modèles et entraînementDeep LearningMultimodal AI+2
1:11:50EN
Lex FridmanPodcastData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the WheelAnime : Lex FridmanIn an MIT lecture, Lex Fridman shows how deep learning can sense pedestrians, driver posture, gaze, emotion and cognitive load, and why that matters for human-centered autonomy.Multimodal et médiasDeep LearningVision et vidéo+3
1:18:48EN
Lex FridmanPodcastData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesAnime : Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.Infrastructure et déploiementInferenceAI cost & performance+3
16:41EN
Lex FridmanPodcastAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and BiologyAnime : Lex FridmanA quick explainer on DeepMind's CASP14 result, how protein folding works, how AlphaFold 1 and 2 likely differ, and what the advance could unlock.Modèles et entraînementAI for scienceDeep Learning+2
1:19:52EN
Lex FridmanPodcastFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End DrivingAnime : Lex FridmanLex Fridman's MIT 6.S094 lecture explains how convolutional neural networks work, where deep learning helps a self-driving car, and how to train a network to steer from video.Modèles et entraînementAutonomous DrivingDeep Learning+3
1:27:30EN
Lex FridmanPodcastFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One LectureAnime : Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.Modèles et entraînementReinforcement LearningDeep Learning+2
1:05:15EN
Lex FridmanPodcastTaming the Long Tail: How Waymo Builds a Self-Driving FactoryAnime : Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.Modèles et entraînementAutonomous DrivingRobotics+3
1:27:15EN
Lex FridmanPodcastPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement LearningAnime : Lex FridmanOpenAI's John Schulman walks through the core methods of deep RL, from the score function estimator to TRPO and DQN, and says when each one is worth using.Modèles et entraînementReinforcement LearningFondamentaux du ML+3
1:19:21EN
Lex FridmanPodcastVapnik's Case for Learning by Predicates, Not Just by More DataAnime : Lex FridmanIn an MIT lecture, Vladimir Vapnik argues that complete statistical learning needs two mechanisms, data and invariants, and that choosing smart predicates is where intelligence lives.Modèles et entraînementFondamentaux du MLAI research+2
1:03:25EN
Lex FridmanPodcastTheano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your MathAnime : Lex FridmanPascal Lamblin of MILA walks through how Theano builds, differentiates, optimizes and runs computation graphs, then trains logistic regression, a convnet and an LSTM.Modèles et entraînementDeep LearningFondamentaux du ML+3
13:58EN
Lex FridmanPodcastDeep Reinforcement Learning, Explained by the Researcher Behind AlphaGoAnime : Lex FridmanDavid Silver of DeepMind walks through what reinforcement learning is, how its building blocks fit together, and why deep neural networks keep surprising even the experts.Modèles et entraînementReinforcement LearningFondamentaux du ML+3
57:55EN
Lex FridmanPodcastFrom Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement LearningAnime : 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.Modèles et entraînementReinforcement LearningDeep Learning+3
17:35EN
Lex FridmanPodcastHands Off the Wheel in San Diego: A Comma Two Demo Drive and a Tour of comma.ai's Hardware ShopAnime : Lex FridmanAfter their podcast conversation, George Hotz takes Lex Fridman on an openpilot drive and then through the comma.ai offices, dashboards, assembly line and compute cluster.Produit et UXAutonomous DrivingAI startups & small teams+3
1:13:22EN
Lex FridmanPodcastThe Second 90%: What It Really Takes to Put Deep Learning on a Driverless CarAnime : 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.Modèles et entraînementAutonomous DrivingRobotics+3
57:15EN
Lex FridmanPodcastInside Torch and Autograd: How Deep Learning Libraries Compute GradientsAnime : Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.Modèles et entraînementFondamentaux du MLDeep Learning+1
1:20:58EN
Lex FridmanPodcastFrom Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email RepliesAnime : 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.Modèles et entraînementDeep LearningFondamentaux du ML+2
1:19:48EN
Lex FridmanPodcastBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep LearningAnime : 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.Modèles et entraînementFondamentaux du MLDeep Learning+3
1:02:06EN
Lex FridmanPodcastLearning TensorFlow Hands-On: From a Mystery Line to Handwritten DigitsAnime : 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.Modèles et entraînementInfrastructure et déploiementOpen-source ecosystems+3
53:13EN
Lex FridmanPodcastHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene SegmentationAnime : 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.Modèles et entraînementComputer VisionDeep Learning+3
34:33EN
Lex FridmanPodcastTeaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous VehiclesAnime : 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.Modèles et entraînementAutonomous DrivingVision et vidéo+3
20:50EN
Lex FridmanPodcastGlitches as Features: What John Hopfield Says Artificial Neural Networks Are MissingAnime : Lex FridmanIn a short conversation with Lex Fridman, physicist and neural-network pioneer John Hopfield compares evolved brains with today's artificial networks and explains why he expects AI to borrow from biology generation after generation.Modèles et entraînementNeuroscience & AIFondamentaux du ML+3
1:31:29EN
Lex FridmanPodcastIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving CarsAnime : 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.Modèles et entraînementDeep LearningAutonomous Driving+3
1:12:00EN
Lex FridmanPodcastWhy Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised LearningAnime : 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.Modèles et entraînementFondamentaux du MLDeep Learning+2
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