筛选结果
347 篇排序
1:27:15EN
Lex Fridman播客Policy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement Learning主持 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播客Vapnik's Case for Learning by Predicates, Not Just by More Data主持 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播客Theano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your Math主持 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 Fridman播客 · 2 人对谈Deep Learning in One Lecture: From Perceptrons to AutoML and the Limits of Learning嘉宾 Deep Learning Basics · 主持 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.
57:25EN
Unsupervised Learning: With Jacob Effron播客From RAG to Reasoning: Why the Future of AI Is a System, Not a Model主持 Jacob EffronDouwe Kiela explains what OpenAI's o1 reveals about test-time compute, why enterprise AI must be specialized, and where post-training and evaluations are heading.
13:58ENLex Fridman播客Deep Reinforcement Learning, Explained by the Researcher Behind AlphaGo主持 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播客From Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement Learning主持 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播客The Second 90%: What It Really Takes to Put Deep Learning on a Driverless Car主持 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播客Inside Torch and Autograd: How Deep Learning Libraries Compute Gradients主持 Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:12:12EN → 简体中文
Lenny's Podcast播客模型是长出来的：Anthropic 研究员如何看见 Claude 的思考过程主持 Lenny RachitskyAnthropic 可解释性研究员 Emmanuel Ameisen 说明，查看语言模型的中间计算，能看见通用概念、提前规划和奇怪故障，这些发现也关系到对齐。
1:20:58ENLex Fridman播客From Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email Replies主持 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播客Bigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep Learning主持 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播客Learning TensorFlow Hands-On: From a Mystery Line to Handwritten Digits主持 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播客Why Simple Theories Win: Marcus Hutter on Occam's Razor, Compression and Kolmogorov Complexity主持 Lex FridmanDeepMind researcher Marcus Hutter explains Solomonoff induction, why compression equals understanding, and how a simple universe can still look rich.
10:34ENLex Fridman播客 · 2 人对谈Why Deep Learning Needs Rules: Chollet on Interpolation, Abstraction and Hybrid AI嘉宾 François Chollet · 主持 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.
1:19:34EN → 简体中文Lenny's Podcast播客李飞飞谈 ImageNet、世界模型，以及为什么每个人都该在 AI 里有位置主持 Lenny Rachitsky常被称为「AI 教母」的科学家，回溯大数据如何点燃深度学习，解释空间智能为何是下一站，并演示 World Labs 的第一款产品 Marble。
53:13ENLex Fridman播客How Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentation主持 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 Fridman播客Teaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous Vehicles主持 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.
1:16:52EN
Cognitive Revolution "How AI Changes Everything"播客Mamba After 90 Days: What 30 Papers Reveal About State Space Models, Hybrids, and Mixture of Experts主持 Nathan LabenzNathan Labenz and AI scout Jason Meaux survey the first wave of Mamba-inspired research, from mechanistic studies and synthetic-task tradeoffs to hybrid architectures and MoE variants.
10:39ENLex Fridman播客Statistics as Decision-Making: Michael I. Jordan on Bayesians, Frequentists and the Ideas Between Them主持 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.
11:29ENLex Fridman播客Why Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has Seen主持 Lex FridmanLex Fridman walks through the neural network, data annotation, Dojo and Tesla Bot announcements and what they say about solving real-world robotics.
52:28ENLex Fridman播客 · 2 人对谈Ray Kurzweil on Hierarchies, the Neocortex and Merging with AI嘉宾 Ray Kurzweil · 主持 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 Fridman播客 · 2 人对谈Circuit Search, Hindsight and Self-Play: Ilya Sutskever's Case for Where Learning Is Going嘉宾 Ilya Sutskever · 主持 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.
1:48:13ENCognitive Revolution "How AI Changes Everything"播客Robotics Hits Its GPT-3.5 Moment: Inside Gemini Robotics, From Embodied Reasoning to Origami主持 Nathan LabenzGoogle DeepMind researchers Keerthana Gopalakrishnan and Ted Xiao explain how Gemini Robotics works, what it can and cannot do, and where the field goes next.加载更多已显示 24 篇，共 347 篇