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1:29:03EN
Lex Fridman· 27 sept 2016PódcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPPresenta: 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· 1 sept 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· 29 ago 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· 5 jul 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· 5 abr 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· 1 abr 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· 31 mar 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· 31 mar 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· 31 mar 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· 31 mar 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· 31 mar 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· 1 nov 2015Toyota Kata for Continuous ImprovementHåkan ForssHåkan Forss explains target conditions, coaching and small experiments.
7:27EN3Blue1Brown· 21 jun 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· 13 may 2015Reinforcement Learning Lecture 5: Model-Free ControlDavid SilverDavid Silver explains Monte Carlo control, SARSA, eligibility traces and Q-learning.
6:13EN3Blue1Brown· 5 mar 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· 16 jun 2014Pódcast · 2 vocesRyan Hall on Principles and Priorities in Jiu-JitsuInvitado: Ryan Hall · Presenta: Lex FridmanA grappling instructor discusses fundamentals, details and learning.
9:16ENLex Fridman· 5 jun 2014Pódcast · 2 vocesRyan Hall on the Value of CompetitionInvitado: Ryan Hall · Presenta: Lex FridmanA martial artist discusses preparation, discipline and honest feedback.
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Lenny's PodcastPódcast · 2 vocesHard Skills, Soft Skills and Mentors: Jules Walter's Playbook for Growing as a PMInvitado: Jules Walter · Presenta: 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 FridmanPódcastDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsPresenta: 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"PódcastFrom Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of PromptingPresenta: 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 PodcastPódcastSkip the Funnel of Doom: Cursor's Playbook for Hiring the Top 50Presenta: 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"PódcastTrust at Scale: Elicit's Founders on Reasoning Languages, World Models and Automating the CompanyPresenta: 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 FridmanPódcastDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroPresenta: 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 PodcastPódcast · 2 vocesPaid Growth, Step by Step: Shopify's Performance Marketing Lead on Tests, Signal and Team DesignInvitado: Timothy Davis · Presenta: 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.Cargar másMostrando 24 de 4485