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Lex Fridman· Sep 27, 2016PodcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPHost Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.
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3Blue1Brown· Sep 1, 2016Why Cross-Product Coordinates Match Their GeometryGrant SandersonA linear transformation and duality connect the determinant formula to area and direction.
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GOTO Conferences· Aug 29, 2016How Platforms Work: From Deployment to RecoveryCasey WestCasey West outlines six platform capabilities and demonstrates them with Cloud Foundry in 2016.
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Google DeepMind· Jul 5, 2016Mustafa Suleyman’s 2016 Vision for Technology in Patient CareMustafa Suleyman · DeepMindA historical talk on clinician-led design, kidney-injury alerts, eye-scan research and the need for scrutiny.
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Jane Street· Apr 5, 2016Yaron Minsky on Effective ML: Writing OCaml for Code ReadersYaron Minsky · Jane StreetPractical lessons from Jane Street on using types, interfaces and abstraction to keep a large OCaml codebase understandable and correct.
16:01EN3Blue1Brown· Apr 1, 2016The Brachistochrone: Why the Fastest Descent Is a CycloidGrant Sanderson, Steven StrogatzGrant Sanderson and Steven Strogatz explore Bernoulli’s light analogy and a geometric proof.
1:06:39ENJane Street· Mar 31, 2016Why Jane Street Uses OCamlYaron Minsky · Jane StreetA Jane Street talk explains how functional programming and static types support reliable trading software, and examines the costs of choosing a less common language.
23:58ENJane Street· Mar 31, 2016Effective ML: Errors, Abstraction and Managing ComplexityYaron Minsky · Jane StreetA Jane Street engineer discusses explicit failure, boilerplate, type-system restraint and the practical role of side effects.
1:04:56ENJane Street· Mar 31, 2016Yaron Minsky on Incremental: From Research Idea to Production LibraryYaron Minsky · Jane StreetA history of the algorithms, memory problems and performance tradeoffs behind Jane Street’s OCaml library for self-adjusting computations.
1:12:41ENJane Street· Mar 31, 2016Ron Minsky on Trading, Correctness and Jane Street’s Shift to OCamlYaron Minsky · Jane StreetA Jane Street technologist explains the market-making business and why readability, types and predictable performance shaped the firm’s choice of OCaml.
51:35ENJane Street· Mar 31, 2016Yaron Minsky on Effective ML: Interfaces, Invariants and Code ReadersYaron Minsky · Jane StreetIn the first part of a 2011 Harvard lecture, Jane Street’s Yaron Minsky shows how OCaml interfaces and types can make long-lived code easier to understand and change.
52:20ENGOTO Conferences· Nov 1, 2015Toyota Kata for Continuous ImprovementHåkan ForssHåkan Forss explains target conditions, coaching and small experiments.
7:27EN3Blue1Brown· Jun 21, 2015A Proof of Euler’s Formula Using Graph DualityA planar graph and its dual have complementary spanning trees. Counting their edges gives a visual proof that V − E + F = 2.
1:36:30ENGoogle DeepMind· May 13, 2015Reinforcement Learning Lecture 5: Model-Free ControlDavid SilverDavid Silver explains Monte Carlo control, SARSA, eligibility traces and Q-learning.
6:13EN3Blue1Brown· Mar 5, 2015Euler’s Identity as a Rotation: An Early 3Blue1Brown ExplanationA geometric introduction to how the exponential connects addition, multiplication and rotations in the complex plane.
9:47ENLex Fridman· Jun 16, 2014Podcast · 2 speakersRyan Hall on Principles and Priorities in Jiu-JitsuGuest Ryan Hall · Host Lex FridmanA grappling instructor discusses fundamentals, details and learning.
9:16ENLex Fridman· Jun 5, 2014Podcast · 2 speakersRyan Hall on the Value of CompetitionGuest Ryan Hall · Host Lex FridmanA martial artist discusses preparation, discipline and honest feedback.
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Lenny's PodcastPodcast · 2 speakersHard Skills, Soft Skills and Mentors: Jules Walter's Playbook for Growing as a PMGuest Jules Walter · Host Lenny RachitskyYouTube product lead Jules Walter explains how he builds IQ and EQ skills, asks for honest feedback, and recruits mentors with the smallest possible ask.
1:00:51ENLex FridmanPodcastDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsHost Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
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Cognitive Revolution "How AI Changes Everything"PodcastFrom Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of PromptingHost Nathan LabenzScale AI's first staff prompt engineer traces how language models went from pure text prediction to instruction following to RLHF, and what that means for building with them.
1:30:46ENLenny's PodcastPodcastSkip the Funnel of Doom: Cursor's Playbook for Hiring the Top 50Host Lenny RachitskyAdam Ward, Cursor's head of talent, explains how to scope, map and relentlessly pursue the best people instead of hiring whoever survives a recruiting funnel.
1:45:49ENCognitive Revolution "How AI Changes Everything"PodcastTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the CompanyHost Nathan LabenzAndreas Stuhlmüller and Jungwon Byun explain how Elicit keeps frontier models checkable for high-stakes science, and where they think AI reasoning is heading.
1:07:29ENLex FridmanPodcastDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroHost Lex FridmanLex Fridman's MIT 6.S091 opening lecture walks from the basic RL framework through DQN, actor-critic methods and AlphaZero to the unsolved problem of moving from simulation to the real world.
1:42:04ENLenny's PodcastPodcast · 2 speakersPaid Growth, Step by Step: Shopify's Performance Marketing Lead on Tests, Signal and Team DesignGuest Timothy Davis · Host Lenny RachitskyTimothy Davis walks through when to start paid, how to run signs-of-life tests, which platforms to try, what to measure and who to hire first.Load moreShowing 24 of 4,485