필터 결과
83개정렬
1:11:50EN
Lex Fridman팟캐스트Data Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the Wheel진행자 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.
10:16ENLex Fridman팟캐스트 · 2명Reasoning Without Logic: Yann LeCun on Teaching Neural Networks to Think게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
1:18:48ENLex Fridman팟캐스트Data Movement, Not Math: How to Make Deep Learning Fit on Robots and Phones진행자 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.
16:41ENLex Fridman팟캐스트AlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and Biology진행자 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.
1:19:52ENLex Fridman팟캐스트From Pixels to Steering Wheels: Convolutional Networks and End-to-End Driving진행자 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.
17:21ENLex Fridman팟캐스트 · 2명How Alexa Learned to Hear From Across the Room: Rohit Prasad on Far-Field Speech and Intent게스트 Rohit Prasad · 진행자 Lex FridmanAlexa's head scientist recalls the early days of the Echo, when far-field speech recognition seemed unsolvable and there were no customers to collect data from.
1:27:30ENLex Fridman팟캐스트From NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One Lecture진행자 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 Fridman팟캐스트Taming the Long Tail: How Waymo Builds a Self-Driving Factory진행자 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 Fridman팟캐스트Policy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement Learning진행자 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 Fridman팟캐스트Vapnik's Case for Learning by Predicates, Not Just by More Data진행자 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 Fridman팟캐스트Theano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Math진행자 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.
1:08:06ENLex Fridman팟캐스트 · 2명Deep Learning in One Lecture: From Perceptrons to AutoML and the Limits of Learning게스트 Deep Learning Basics · 진행자 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.
13:58ENLex Fridman팟캐스트Deep Reinforcement Learning, Explained by the Researcher Behind AlphaGo진행자 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 Fridman팟캐스트From Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement Learning진행자 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.
17:35ENLex Fridman팟캐스트Hands Off the Wheel in San Diego: A Comma Two Demo Drive and a Tour of comma.ai's Hardware Shop진행자 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.
1:13:22ENLex Fridman팟캐스트The Second 90%: What It Really Takes to Put Deep Learning on a Driverless Car진행자 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 Fridman팟캐스트Inside Torch and Autograd: How Deep Learning Libraries Compute Gradients진행자 Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
24:08ENLex Fridman팟캐스트 · 2명Intelligence Is Not Just Prediction: David Ferrucci on Explanation, Persuasion and Shared Meaning게스트 David Ferrucci · 진행자 Lex FridmanThe lead of IBM's Watson team argues that predicting well is only half of intelligence, and that being understood by others is the other half.
1:20:58ENLex Fridman팟캐스트From Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Replies진행자 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 Fridman팟캐스트Bigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learning진행자 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 Fridman팟캐스트Learning TensorFlow Hands-On: From a Mystery Line to Handwritten Digits진행자 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.
10:34ENLex Fridman팟캐스트 · 2명Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI게스트 François Chollet · 진행자 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.
53:13ENLex Fridman팟캐스트How Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentation진행자 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 Fridman팟캐스트Teaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous Vehicles진행자 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.더 보기83개 중 24개 표시