Jethro's Braindump

Zettels (323)

  1. Learning How To Do Hard Things
  2. John Wiegley
  3. Multi-modal Fusion
  4. Statistical Testing
  5. Common Statistical Tests Are Linear Models
  6. Martin Kleppmann
  7. Coding Interview Preparation
  8. Designing Data-Intensive Applications
  9. Unsupervised Learning
  10. Conditional Random Fields
  11. Multi-modal Autoencoders
  12. Multiple Learning Kernel
  13. Support Vector Machines
  14. Multi-modal Machine Learning
  15. Zero shot Learning
  16. Concept Grounding
  17. Co-learning
  18. Multi-modal Representation
  19. Cross-modal Hashing
  20. Multi-modal Translation
  21. LaTeX
  22. Generative Models
  23. Multi-modal Alignment
  24. Dynamic Time Warping
  25. Canonical Correlation Analysis
  26. Deep Boltzmann Machines
  27. Restricted Boltzmann machines
  28. Autoencoder
  29. Interval Estimation in Bayesian Statistics
  30. Kl Divergence
  31. Gibbs' Inequality
  32. Celeste Kidd
  33. How To Know - Celeste Kidd
  34. NeurIPS
  35. Information Theory
  36. Research
  37. Article: An Opinionated Guide to ML Research
  38. John Schulman
  39. Fisher information
  40. Jeffreys Prior
  41. Reference Prior
  42. Change of Variables Theorem
  43. Non-informative Priors
  44. Definition of Deep Learning
  45. Yann LeCun
  46. Git
  47. Git Scalar
  48. VFS for Git
  49. Version Control
  50. Roam Research
  51. Reading
  52. How To Read A Book
  53. Feynman Technique
  54. Learning
  55. Learning How To Learn
  56. Learning Complex Information
  57. Zettelkasten
  58. How To Take Smart Notes
  59. Sonke Ahrens
  60. Tom Tromey
  61. Robotics Algorithms
  62. Web Performance
  63. Recommender Systems
  64. Stochastic Processes
  65. Reinforcement Learning ⭐
  66. Writing with Zettekasten
  67. Richard Feynman
  68. Rejection Sampling
  69. Emacs Lisp
  70. Elisp: Buffer-passing Style
  71. Mental Models
  72. Note-taking
  73. Asian Cinema
  74. Org-Roam
  75. Slice Sampling
  76. Monte Carlo Methods
  77. Gibbs Sampling
  78. How To Write a Technical Paper
  79. Nat Eliason
  80. Conor White-Sullivan
  81. Documentation Generators
  82. JavaScript
  83. Python
  84. Variational Autoencoders
  85. Travel
  86. Find (CLI Tool)
  87. Unix
  88. Lsof
  89. Awk
  90. Nix/NixOS
  91. Documentation
  92. Spaced Repetition
  93. Docker 101
  94. GCC
  95. Getting Things Done (GTD)
  96. Productivity
  97. Zeigarnik Effect
  98. Spike Train Metrics
  99. SNN Software
  100. Kalman Filter
  101. Information Filter
  102. Extended Kalman Filter
  103. Policy Gradients
  104. Deep Reinforcement Learning
  105. Q-Learning
  106. Model-Based Reinforcement Learning
  107. Transfer Learning
  108. Distributed Reinforcement Learning
  109. Exploration In Reinforcement Learning
  110. Information-Theoretic Reinforcement Learning
  111. Rademacher Complexity
  112. Gaussian Filter
  113. Leslie Lamport
  114. Machine Learning
  115. Riken AIP Workshop 2019
  116. Emtiyaz Khan
  117. PAC Learning
  118. Empirical Risk Minimization
  119. Bayes Filter
  120. Programming Languages
  121. Emacs
  122. Neuroscience and Reinforcement Learning
  123. Control As Inference
  124. Papers
  125. Neuroscience ⭐
  126. Human Behaviour As Optimal Control
  127. Generalization In Reinforcement Learning
  128. Meta Learning
  129. Inverse Reinforcement Learning
  130. Free-Energy Reinforcement Learning
  131. Machine Learning Papers
  132. Entropy
  133. VC-Dimension
  134. Motion Model With Maps
  135. Velocity Motion Model
  136. Quantization
  137. And the Bit Goes Down: Revisiting the Quantization of Neural Networks
  138. Actor-Critic
  139. Metropolis-Hastings Method
  140. Org-Mode
  141. Linux
  142. CSS
  143. Matplotlib
  144. Branch Prediction
  145. Conferences
  146. Soft Skills
  147. ARM Assembly Programming
  148. Bayesian Statistics
  149. I-Diagrams
  150. Model Compression
  151. CMake
  152. Markov Chains
  153. Regression
  154. Computer Organization
  155. Point Estimation in Bayesian Statistics
  156. Likelihood Principle
  157. Statistical Learning
  158. Hadoop
  159. Arguments Against Bayesian Inference
  160. Neural Network Optimizer
  161. Large Batch Training
  162. Fast Neural Network Training
  163. Exponential Family
  164. Sufficient Statistics
  165. Numpy
  166. Java
  167. Computer Networking
  168. Probability Theory
  169. PDF Nup
  170. Pdf Tools
  171. PDF Cropping
  172. Occam's Razor
  173. Systems Programming
  174. Jensen's Inequality
  175. Feedback Alignment and Random Error Backpropagation
  176. Random Variables
  177. XGBoost
  178. Variational Inference
  179. Deep Learning Tools
  180. Statistics
  181. Normalizing Flows
  182. Machine Learning Algorithms
  183. Expectation Maximization and Mixture Models
  184. System Design
  185. Neuroscience Experimental Evidence
  186. Synaptic Current Model
  187. Evolving Connectionist Systems
  188. Evolving Spiking Neural Networks
  189. Leaky Integrate-And-Fire
  190. Writing
  191. Writing Articles
  192. Presentations
  193. Writing Books
  194. Copy Editing
  195. Cognitive Hierarchy Model
  196. Artificial Intelligence
  197. Probabilistic Graph Models
  198. Natural Language Processing
  199. API Design
  200. Code Litmus Tests
  201. Game API Design
  202. Config Management
  203. Bayesian Deep Learning
  204. Smoothed Spiking Neural Networks
  205. SSNLP Conference Notes
  206. Datacouncil.ai Conference Notes
  207. Topic Modeling
  208. Negotiation
  209. Studying
  210. Arguments Against Zettelkasten
  211. PARA Method
  212. Progressive Summarization
  213. Databases
  214. Machine Teaching
  215. Spiking Neural Networks
  216. Scala
  217. Spark
  218. Web Development
  219. Web Dev Tools
  220. HTTP
  221. React
  222. Swift
  223. Haskell
  224. OCaml
  225. The C Language
  226. Books
  227. Spike Train Mutual Information
  228. Spiking Neurons (Literature Review)
  229. Spiking Datasets
  230. LARS Optimizer
  231. Gpipe
  232. Information Bottleneck in Deep Neural Networks
  233. Bayesian Inference
  234. LeCun's Cake Analogy
  235. Multi-variable Calculus
  236. Credit Assignment in Spiking Neural Networks
  237. Collaborative Editing
  238. Anti-fragile Ideas
  239. Gaussian Processes
  240. Wisdom
  241. Particle Filter
  242. Importance Sampling
  243. Two Levels Of Inference
  244. Laplace's Method
  245. Art
  246. Hidden Markov Model
  247. Optimal Control and Planning
  248. Monte Carlo Tree Search
  249. Experience Replay
  250. Imitation Learning
  251. Generalized Value Functions
  252. Options Framework
  253. Simultaneous Localization and Mapping (SLAM)
  254. Temporal Difference Learning
  255. Markov Decision Process
  256. Partially Observable Markov Decision Processes (POMDPs)
  257. Running
  258. Odometry Motion Model
  259. RSS Feeds
  260. LU Decomposition
  261. Google Cartographer
  262. Robotics
  263. Occupancy Grid Mapping
  264. Markov Localization
  265. Robot Localization
  266. Grid & Monte Carlo Localization
  267. EKF Localization
  268. State Estimation
  269. Likelihood Field Model
  270. Map Matching
  271. Range Finder Model
  272. Robot Kinematics
  273. Portfolio Composition
  274. Investment
  275. Investing In ETFs
  276. Robot Motion
  277. Non-parametric Filters
  278. Histogram Filter
  279. Markovian Assumption
  280. Robotics Probabilistic Generative Laws
  281. Uncertainty in Robotics
  282. Compilers
  283. Robot Operating System (ROS)
  284. Sleep
  285. Statistical Methods for Finance
  286. Fitness
  287. Expectation Maximization and Mixture Models
  288. Hopfield Network
  289. Ising Models
  290. The Bias-Complexity Tradeoff
  291. C++
  292. Hacking
  293. Formulation
  294. Optimization
  295. Linear Algebra
  296. Computer Vision
  297. Theory Of Computation
  298. BitTorrent
  299. Cryptography
  300. Data Science
  301. Software Engineering
  302. Operating Systems
  303. Data Visualization
  304. Data Structures and Algorithms
  305. Programming Methodology
  306. Blockchain
  307. Deep Learning
  308. Google Cloud Platform
  309. Singapore Society
  310. Trigger List
  311. IS1103: Computing and Society
  312. Consciousness
  313. Podcasts
  314. Security
  315. Ideas
  316. Critical Thinking
  317. Weekly Review
  318. Conversation
  319. Dev Ops
  320. Daily Ritual
  321. Quantitative Reasoning
  322. General Links
  323. iOS

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