絞り込み結果
51 件並び順
1:15:59EN
Lex FridmanポッドキャストSteering Through Time: How Recurrent Networks and LSTMs Learn from Sequencesホスト Lex FridmanLex Fridman's MIT 6.S094 lecture walks from backpropagation basics to LSTMs and the way they are used to predict steering in self-driving cars.
37:00ENLex FridmanポッドキャストInside the YouTube Algorithm: Related Graphs, Satisfaction Surveys and the Viewers Who Are Part of the Codeホスト 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.
1:31:52ENLex FridmanポッドキャストFrom Phonemes to CTC: How Deep Learning Rebuilt Speech Recognitionホスト 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.
17:40ENLex FridmanポッドキャストFrom AlphaGo Zero to MuZero: How Self-Play Strips Knowledge Out of AIホスト Lex FridmanDeepMind's David Silver explains why removing human knowledge made AlphaZero stronger and more general, and how MuZero extended the idea to worlds with unknown rules.
1:07:35ENLex FridmanポッドキャストWhy AI Will Never Be Perfect: Lex Fridman's Case for Keeping Humans in the Loopホスト Lex FridmanIn MIT 6.S093's opening lecture, Lex Fridman lays out five research areas of human-centered AI, from machine teaching to arguing machines that flag their own uncertainty.
1:19:52ENLex FridmanポッドキャストFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End Drivingホスト 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.
1:27:30ENLex FridmanポッドキャストFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One Lectureホスト Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.
1:05:15ENLex FridmanポッドキャストTaming the Long Tail: How Waymo Builds a Self-Driving Factoryホスト Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.
1:27:15ENLex FridmanポッドキャストPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement Learningホスト 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.
1:19:21ENLex FridmanポッドキャストVapnik's Case for Learning by Predicates, Not Just by More Dataホスト 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.
1:03:25ENLex FridmanポッドキャストTheano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Mathホスト 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.
13:58ENLex FridmanポッドキャストDeep Reinforcement Learning, Explained by the Researcher Behind AlphaGoホスト 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.
57:55ENLex FridmanポッドキャストFrom Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement Learningホスト 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 FridmanポッドキャストThe Second 90%: What It Really Takes to Put Deep Learning on a Driverless Carホスト 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 FridmanポッドキャストInside Torch and Autograd: How Deep Learning Libraries Compute Gradientsホスト Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:12:12EN
Lenny's PodcastポッドキャストModels Are Grown, Not Built: What Anthropic's Interpretability Research Sees Inside Claudeホスト Lenny RachitskyAnthropic researcher Emmanuel Ameisen explains how studying a language model's intermediate computations reveals concepts, plans and surprising quirks, and why that matters for alignment.
1:20:58ENLex FridmanポッドキャストFrom Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Repliesホスト 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 FridmanポッドキャストBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learningホスト 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 FridmanポッドキャストLearning TensorFlow Hands-On: From a Mystery Line to Handwritten Digitsホスト 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.
20:36ENLex FridmanポッドキャストWhy Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov Complexityホスト Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.
10:39ENLex FridmanポッドキャストStatistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Themホスト 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.
20:50ENLex FridmanポッドキャストGlitches as Features: What John Hopfield Says Artificial Neural Networks Are Missingホスト 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.
1:03:10EN
Cognitive Revolution "How AI Changes Everything"ポッドキャストPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next Upgradesホスト Nathan LabenzNathan Labenz and Trey Kollmer walk through five recent papers on pausing, backspacing, prompting, streaming and long-context training, and sketch how they might combine into a more capable model.
1:31:29ENLex FridmanポッドキャストIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Carsホスト 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.もっと見る51 件中 24 件を表示