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슬라이드 7장25:44EN
Jane Street· 2017년 3월 9일Why Functional Programming Matters at Jane StreetSebastian Funk · Jane StreetA Jane Street developer revisits John Hughes’s classic argument and explains why expressive static types rank highest for trading systems.Programming LanguagesSoftware engineeringCoding Culture
슬라이드 8장28:31EN
Jane Street· 2017년 3월 9일Incremental: Self-Adjusting Computations in OCamlSebastian Funk · Jane StreetSebastian explains Jane Street’s dynamic dependency graph library and the algorithms behind efficient updates.Programming LanguagesSoftware engineeringDeveloper productivity
슬라이드 4장13:58EN
3Blue1Brown· 2016년 11월 25일Solving the Towers of Hanoi by Counting in BinaryKeith SchwarzBinary carries select the disk to move, and a recursive argument explains why the solution uses the fewest possible moves.Mathematics
16:29EN
3Blue1Brown· 2016년 11월 4일Why Topology Matters: An Inscribed Rectangle ArgumentGrant SandersonGrant Sanderson turns pairs of points on a curve into a Möbius strip.Mathematics
1:29:03EN
Lex Fridman· 2016년 9월 27일팟캐스트From Word Vectors to Memory Networks: The Building Blocks of Deep NLP진행자 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.모델과 학습Deep Learning머신러닝 기초+2
13:10EN
3Blue1Brown· 2016년 9월 1일Why Cross-Product Coordinates Match Their GeometryGrant SandersonA linear transformation and duality connect the determinant formula to area and direction.MathematicsEducation
43:16EN
GOTO Conferences· 2016년 8월 29일How Platforms Work: From Deployment to RecoveryCasey WestCasey West outlines six platform capabilities and demonstrates them with Cloud Foundry in 2016.기업과 전략Company Building
30:03EN
Google DeepMind· 2016년 7월 5일Mustafa 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.AI in healthcareAI for sciencePrivacy & data governance+1
슬라이드 3장1:09:24EN
Jane Street· 2016년 4월 5일Yaron 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.Programming LanguagesSoftware engineeringCode review & verification+1
16:01EN
3Blue1Brown· 2016년 4월 1일The Brachistochrone: Why the Fastest Descent Is a CycloidGrant Sanderson, Steven StrogatzGrant Sanderson and Steven Strogatz explore Bernoulli’s light analogy and a geometric proof.MathematicsPhysics
슬라이드 3장1:06:39EN
Jane Street· 2016년 3월 31일Why 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.Programming LanguagesSoftware engineeringDeveloper productivity+1
23:58EN
Jane Street· 2016년 3월 31일Effective 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.Programming LanguagesSoftware engineeringCode review & verification
슬라이드 2장1:04:56EN
Jane Street· 2016년 3월 31일Yaron 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.Programming LanguagesSoftware engineeringDeveloper productivity
슬라이드 3장1:12:41EN
Jane Street· 2016년 3월 31일Ron 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.Programming LanguagesSoftware engineeringInvesting
슬라이드 5장51:35EN
Jane Street· 2016년 3월 31일Yaron 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.Programming LanguagesSoftware engineeringCode review & verification
52:20EN
GOTO Conferences· 2015년 11월 1일Toyota Kata for Continuous ImprovementHåkan ForssHåkan Forss explains target conditions, coaching and small experiments.ManagementSoftware engineeringExperimentation
슬라이드 4장7:27EN
3Blue1Brown· 2015년 6월 21일A 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.MathematicsScience Communication
1:36:30EN
Google DeepMind· 2015년 5월 13일Reinforcement Learning Lecture 5: Model-Free ControlDavid SilverDavid Silver explains Monte Carlo control, SARSA, eligibility traces and Q-learning.Reinforcement LearningAI research
6:13EN
3Blue1Brown· 2015년 3월 5일Euler’s Identity as a Rotation: An Early 3Blue1Brown ExplanationA geometric introduction to how the exponential connects addition, multiplication and rotations in the complex plane.Mathematics
9:47EN
Lex Fridman· 2014년 6월 16일팟캐스트 · 2명Ryan Hall on Principles and Priorities in Jiu-Jitsu게스트 Ryan Hall · 진행자 Lex FridmanA grappling instructor discusses fundamentals, details and learning.
9:16EN
Lex Fridman· 2014년 6월 5일팟캐스트 · 2명Ryan Hall on the Value of Competition게스트 Ryan Hall · 진행자 Lex FridmanA martial artist discusses preparation, discipline and honest feedback.
1:10:27EN
Lenny's Podcast팟캐스트 · 2명Hard Skills, Soft Skills and Mentors: Jules Walter's Playbook for Growing as a PM게스트 Jules Walter · 진행자 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.제품과 UXProduct ManagementLeadership+3
1:00:51EN
Lex Fridman팟캐스트Deep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network Foundations진행자 Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.모델과 학습Deep Learning머신러닝 기초+1
1:56:35EN
Cognitive Revolution "How AI Changes Everything"팟캐스트From Autocomplete to Alien Optimizer: Riley Goodside on Three Eras of Prompting진행자 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.모델과 학습Prompting & in-context learningPrompt injection+2
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