Resultados · YoutuBlog

Canales actualizados recientemente

Elige uno para ver solo ese canalVer todos los canales →

Matthew Bermanayer +478

Theo - t3․ggayer +33

AI Engineerayer +6

The Buildersayer +1

硅谷101ayer +1

SemiAnalysisayer +1

Chris Williamsonayer

Cheeky Pintayer

Invest Like The Bestayer

Resultados

101 artículos

Ordenar

TemaFundamentos de ML

Lex FridmanPódcastFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One LecturePresenta: Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.Modelos y entrenamientoReinforcement LearningDeep Learning+2

Lex FridmanPódcastTaming the Long Tail: How Waymo Builds a Self-Driving FactoryPresenta: Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.Modelos y entrenamientoAutonomous DrivingRobotics+3

Lex FridmanPódcastPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement LearningPresenta: 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.Modelos y entrenamientoReinforcement LearningFundamentos de ML+3

Lex FridmanPódcastVapnik's Case for Learning by Predicates, Not Just by More DataPresenta: 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.Modelos y entrenamientoFundamentos de MLAI research+2

Lex FridmanPódcastTheano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your MathPresenta: 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.Modelos y entrenamientoDeep LearningFundamentos de ML+3

Lex FridmanPódcast · 2 vocesDeep Learning in One Lecture: From Perceptrons to AutoML and the Limits of LearningInvitado: Deep Learning Basics · Presenta: Lex FridmanLex Fridman's opening lecture for MIT's 6.S094 course surveys why deep learning took off, how neural networks learn, and where the technique still falls short.Modelos y entrenamientoDeep LearningFundamentos de ML+3

Lex FridmanPódcastDeep Reinforcement Learning, Explained by the Researcher Behind AlphaGoPresenta: 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.Modelos y entrenamientoReinforcement LearningFundamentos de ML+3

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.Modelos y entrenamientoReinforcement LearningDeep Learning+3

Lex 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.Modelos y entrenamientoAutonomous DrivingRobotics+3

Lex 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.Modelos y entrenamientoFundamentos de MLDeep Learning+1

Lenny's PodcastPódcastModels Are Grown, Not Built: What Anthropic's Interpretability Research Sees Inside ClaudePresenta: 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.Modelos y entrenamientoModel InterpretabilityAI research+3

Lex 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.Modelos y entrenamientoDeep LearningFundamentos de ML+2

Lex 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.Modelos y entrenamientoFundamentos de MLDeep Learning+3

Lex 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.Modelos y entrenamientoInfraestructura y despliegueOpen-source ecosystems+3

Lex FridmanPódcastWhy Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov ComplexityPresenta: Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.Modelos y entrenamientoFundamentos de MLAI research+3

Lex FridmanPódcast · 2 vocesWhy Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AIInvitado: François Chollet · Presenta: Lex FridmanFrançois Chollet explains why neural networks only interpolate between points they have seen, and why the best AI systems pair them with symbolic rules.Modelos y entrenamientoFundamentos de MLDeep Learning+2

Lex FridmanPódcastStatistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between ThemPresenta: 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.Modelos y entrenamientoFundamentos de MLDecision Making+2

Lex 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.Modelos y entrenamientoFundamentos de MLDeep Learning+3

Lex FridmanPódcastGlitches as Features: What John Hopfield Says Artificial Neural Networks Are MissingPresenta: 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.Modelos y entrenamientoNeuroscience & AIFundamentos de ML+3

Lex FridmanPódcast · 2 vocesReverse-Engineering the Child: Josh Tenenbaum's Case for Human-Like AIInvitado: MIT AGI · Presenta: Lex FridmanMIT cognitive scientist Josh Tenenbaum argues that real machine intelligence needs models of the world, built by studying how babies think and learn, not just better pattern recognition.Modelos y entrenamientoAGIFundamentos de ML+3

Lex FridmanPódcast · 2 vocesFrom a Side Project to TensorFlow's Core: François Chollet on How Keras Came to BeInvitado: François Chollet · Presenta: Lex FridmanThe creator of Keras recalls why he built it in 2015 around recurrent networks, how it moved onto TensorFlow, and what TensorFlow 2.0 means for its design.Código abierto y comunidadDeep LearningOpen-source ecosystems+3

Cognitive Revolution "How AI Changes Everything"PódcastPause Tokens, Analogical Prompts and Ring Attention: A Tour of the Transformer's Next UpgradesPresenta: 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.Modelos y entrenamientoTransformers & attentionFundamentos de ML+3

Lex FridmanPódcast · 2 vocesStart Small, Build Habits, Pick Your Team: Andrew Ng's Practical Advice for Learning Deep LearningInvitado: Andrew Ng · Presenta: Lex FridmanIn a clip from the Lex Fridman Podcast, Andrew Ng explains how to begin in deep learning, how to study effectively, and how to choose a career path.Modelos y entrenamientoDeep LearningFundamentos de ML+3

Lex 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.Modelos y entrenamientoDeep LearningAutonomous Driving+3

Cargar másMostrando 24 de 101