Jethro's Braindump

Zettels (364)

  1. The C Language
  2. C++ Language
  3. Statistical Distributions
  4. Michael Nielsen
  5. Operating Systems
  6. What happens when we type a simple command on shell?
  7. PARA Method
  8. Richard Hamming
  9. Shell
  10. Progressive Summarization
  11. Emacs Lisp
  12. Large Batch Training
  13. Machine Learning
  14. Emacs
  15. Elisp: Buffer-passing Style
  16. Bayesian Statistics
  17. Fast Neural Network Training
  18. Org-Roam
  19. Gibbs' Inequality
  20. Numpy
  21. Riken AIP Workshop 2019
  22. Documentation Generators
  23. Expectation Maximization and Mixture Models
  24. Gibbs Sampling
  25. Sufficient Statistics
  26. Unix
  27. Bayesian Inference
  28. Business
  29. NeurIPS
  30. Zeigarnik Effect
  31. Machine Teaching
  32. Options Framework
  33. Gpipe
  34. Negotiation
  35. Odometry Motion Model
  36. Probability Theory
  37. Co-learning
  38. Coding Interview Cheatsheet
  39. Cognitive Hierarchy Model
  40. Markov Decision Process
  41. Statistical Methods for Finance
  42. Deep Boltzmann Machines
  43. Definition of Deep Learning
  44. John Schulman
  45. Reinforcement Learning ⭐
  46. Slice Sampling
  47. Spiking Neurons (Literature Review)
  48. ARM Assembly Programming
  49. Coding Interview Preparation
  50. Java
  51. Martin Kleppmann
  52. Three-pass Technique
  53. Hopfield Network
  54. How To Take Smart Notes
  55. Neuroscience ⭐
  56. Topic Modeling
  57. Compilers
  58. Model-Based Reinforcement Learning
  59. Computer Networking
  60. Investing In ETFs
  61. Designing Data-Intensive Applications
  62. Gaussian Filter
  63. Particle Filter
  64. Scala
  65. Travel
  66. Building a PC
  67. LaTeX
  68. Markov Chains
  69. Multi-modal Alignment
  70. Programming Methodology
  71. Robot Localization
  72. Mental Models
  73. Occam's Razor
  74. Red-Black Tree
  75. Spiking Datasets
  76. Bayesian Deep Learning
  77. Empirical Risk Minimization
  78. Ising Models
  79. Machine Learning Papers
  80. Leaky Integrate-And-Fire
  81. Multi-modal Autoencoders
  82. Multi-variable Calculus
  83. Version Control
  84. Conversation
  85. Google Cloud Platform
  86. Investment
  87. Tom Tromey
  88. VFS for Git
  89. Zero shot Learning
  90. Data Structures and Algorithms
  91. Information Theory
  92. Reference Prior
  93. Autoencoder
  94. Experience Replay
  95. Laplace's Method
  96. Policy Gradients
  97. Robot Motion
  98. Find (CLI Tool)
  99. Learning How To Learn
  100. LU Decomposition
  101. Research
  102. Change of Variables Theorem
  103. Gaussian Processes
  104. GCC
  105. Markov Localization
  106. Artificial Intelligence
  107. Rover
  108. Fisher information
  109. Software Engineering
  110. Spaced Repetition
  111. Yann LeCun
  112. Books
  113. PAC Learning
  114. CMake
  115. Partially Observable Markov Decision Processes (POMDPs)
  116. Probabilistic Graph Models
  117. Uncertainty in Robotics
  118. Consciousness
  119. Productivity
  120. SNN Software
  121. Wisdom
  122. Haskell
  123. Rademacher Complexity
  124. Richard Feynman
  125. Lsof
  126. Zettelkasten
  127. Dynamic Time Warping
  128. Jeffreys Prior
  129. LeCun's Cake Analogy
  130. Soft Skills
  131. Spark
  132. Conor White-Sullivan
  133. Fitness
  134. I-Diagrams
  135. Neuroscience and Reinforcement Learning
  136. Simultaneous Localization and Mapping (SLAM)
  137. Model Compression
  138. Programming Languages
  139. Range Finder Model
  140. Trigger List
  141. Writing
  142. Dev Ops
  143. Human Behaviour As Optimal Control
  144. Likelihood Field Model
  145. Linux
  146. Multi-modal Representation
  147. PDF Nup
  148. Asian Cinema
  149. Feynman Technique
  150. Imitation Learning
  151. Importance Sampling
  152. Smoothed Spiking Neural Networks
  153. Transfer Learning
  154. Anti-fragile Ideas
  155. Art
  156. Learning
  157. Restricted Boltzmann machines
  158. Unsupervised Learning
  159. Extended Kalman Filter
  160. Pdf Tools
  161. Statistics
  162. Config Management
  163. Generative Models
  164. Roam Research
  165. Running
  166. Stochastic Processes
  167. VC-Dimension
  168. BitTorrent
  169. Interval Estimation in Bayesian Statistics
  170. Robot Kinematics
  171. Robotics
  172. Systems Programming
  173. Article: An Opinionated Guide to ML Research
  174. Theory Of Computation
  175. Git Scalar
  176. Jensen's Inequality
  177. Computer Organization
  178. Distributed Reinforcement Learning
  179. Nix/NixOS
  180. Reading
  181. Two Levels Of Inference
  182. Code Litmus Tests
  183. HTTP
  184. Metropolis-Hastings Method
  185. PDF Cropping
  186. Variational Inference
  187. Concept Grounding
  188. Courtland Allen
  189. Entropy
  190. Evolving Connectionist Systems
  191. Synaptic Current Model
  192. Regression
  193. Generalization In Reinforcement Learning
  194. Quantitative Reasoning
  195. React
  196. Web Performance
  197. Evolving Spiking Neural Networks
  198. Swift
  199. Variational Autoencoders
  200. Writing Articles
  201. Histogram Filter
  202. John Wiegley
  203. Optimal Control and Planning
  204. Awk
  205. Cross-modal Hashing
  206. Feedback Alignment and Random Error Backpropagation
  207. How To Read A Book
  208. Monte Carlo Methods
  209. Studying
  210. Credit Assignment in Spiking Neural Networks
  211. Generalized Value Functions
  212. Kalman Filter
  213. The Bias-Complexity Tradeoff
  214. Blockchain
  215. Git
  216. Map Matching
  217. Meta Learning
  218. Multiple Learning Kernel
  219. Point Estimation in Bayesian Statistics
  220. Free-Energy Reinforcement Learning
  221. Hacking
  222. Inverse Reinforcement Learning
  223. Kl Divergence
  224. Multi-modal Machine Learning
  225. Celeste Kidd
  226. Conferences
  227. Neuroscience Experimental Evidence
  228. Org-Mode
  229. Papers
  230. Random Variables
  231. Bloom Filter
  232. Cuckoo Filters
  233. Exploration In Reinforcement Learning
  234. How To Write a Technical Paper
  235. Information-Theoretic Reinforcement Learning
  236. Spiking Neural Networks
  237. SSNLP Conference Notes
  238. State Estimation
  239. Canonical Correlation Analysis
  240. Grid & Monte Carlo Localization
  241. Occupancy Grid Mapping
  242. Quantization
  243. Spike Train Mutual Information
  244. Non-parametric Filters
  245. Robot Operating System (ROS)
  246. CSS
  247. Hidden Markov Model
  248. Matplotlib
  249. Nat Eliason
  250. RSS Feeds
  251. Sonke Ahrens
  252. Cryptography
  253. Learning How To Do Hard Things
  254. Non-informative Priors
  255. Writing Books
  256. Emtiyaz Khan
  257. Natural Language Processing
  258. OCaml
  259. Security
  260. Data Science
  261. Google Cartographer
  262. IS1103: Computing and Society
  263. Neural Network Optimizer
  264. Optimization
  265. Attention (ML)
  266. Information Filter
  267. Spike Train Metrics
  268. Conditional Random Fields
  269. Deep Reinforcement Learning
  270. Documentation
  271. Experimental Data Science
  272. Information Bottleneck in Deep Neural Networks
  273. Portfolio Composition
  274. Common Statistical Tests Are Linear Models
  275. Databases
  276. Monte Carlo Tree Search
  277. t-distribution
  278. XGBoost
  279. And the Bit Goes Down: Revisiting the Quantization of Neural Networks
  280. EKF Localization
  281. Exponential Family
  282. Likelihood Principle
  283. Q-Learning
  284. Bayes Filter
  285. Critical Thinking
  286. How To Know - Celeste Kidd
  287. Sleep
  288. Support Vector Machines
  289. Arguments Against Zettelkasten
  290. Getting Things Done (GTD)
  291. Python
  292. Web Dev Tools
  293. Web Development
  294. Actor-Critic
  295. Branch Prediction
  296. Data Visualization
  297. Presentations
  298. Linear Algebra
  299. Statistical Learning
  300. Arguments Against Bayesian Inference
  301. Hadoop
  302. JavaScript
  303. Multi-modal Fusion
  304. Normalizing Flows
  305. Collaborative Editing
  306. Deep Learning Tools
  307. Robotics Algorithms
  308. Temporal Difference Learning
  309. Computer Vision
  310. Multi-modal Translation
  311. Deep Learning
  312. Game API Design
  313. Markovian Assumption
  314. Note-taking
  315. Podcasts
  316. Datacouncil.ai Conference Notes
  317. LARS Optimizer
  318. Singapore Society
  319. Copy Editing
  320. Docker 101
  321. Leslie Lamport
  322. Statistical Testing
  323. Writing with Zettekasten
  324. Control As Inference
  325. Machine Learning Algorithms
  326. Robotics Probabilistic Generative Laws
  327. System Design
  328. Velocity Motion Model
  329. Motion Model With Maps
  330. Recommender Systems
  331. Rejection Sampling
  332. SimCLR
  333. How to brainstorm great business ideas
  334. The Art Of Doing Science And Engineering
  335. The Art Of Unix Programming
  336. Are We Smart Enough to Know How Smart Animals Are?
  337. Work Clean
  338. Neural Ordinary Differential Equations (Review)
  339. Hindsight Experience Replay
  340. Single Layer XOR
  341. A critique of pure learning and what artificial neural networks can learn from animal brains
  342. Emacs Should Be Emacs Lisp - Tom Tromey
  343. Markov Logic Networks
  344. If You're Not Writing a Programming Language, Don't Use A Programming Language - Leslie Lamport
  345. A Distributional Code for Value in Dopamine-based Reinforcement Learning
  346. Surrogate Gradient Learning In Spiking Neural Networks
  347. Deep Reinforcement Learning That Matters
  348. Differentiable plasticity: training plastic neural networks with backpropagation
  349. Temp Coding with Alpha Synaptic Function
  350. Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches
  351. Playing Atari with Deep RL
  352. Learning How To Do Hard Things
  353. Change of Variables Theorem
  354. Learning Complex Information
  355. Asian Cinema
  356. Information-Theoretic Reinforcement Learning
  357. API Design
  358. Expectation Maximization and Mixture Models
  359. Formulation
  360. Ideas
  361. Weekly Review
  362. Daily Ritual
  363. General Links
  364. iOS

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