Jethro's Braindump

Zettels (215)

  1. Exponential Family
  2. Interval Estimation in Bayesian Statistics
  3. Point Estimation in Bayesian Statistics
  4. Sufficient Statistics
  5. Progressive Summarization
  6. PARA Method
  7. Spiking Neurons (Literature Review)
  8. LeCun's Cake Analogy
  9. Free-Energy Reinforcement Learning
  10. SNN Software
  11. Spiking Datasets
  12. Spiking Neural Networks
  13. Neuroscience Experimental Evidence
  14. Feedback Alignment and Random Error Backpropagation
  15. Surrogate Gradients in Spiking Neural Networks
  16. Smoothed Spiking Neural Networks
  17. Bayesian Inference
  18. Multi-variable Calculus
  19. Credit Assignment in Spiking Neural Networks
  20. Synaptic Current Model
  21. Leaky Integrate-And-Fire
  22. Evolving Spiking Neural Networks
  23. Collaborative Editing
  24. Bayesian Deep Learning
  25. Anti-fragile Ideas
  26. Occam's Razor
  27. Gaussian Processes
  28. I-Diagrams
  29. Wisdom
  30. Metropolis-Hastings Method
  31. Evolving Connectionist Systems
  32. Information Theory
  33. Slice Sampling
  34. Monte Carlo Methods
  35. Gibbs Sampling
  36. Rejection Sampling
  37. Particle Filter
  38. Importance Sampling
  39. Neuroscience and Reinforcement Learning
  40. Two Levels Of Inference
  41. Laplace's Method
  42. Config Management
  43. Arguments Against Bayesian Inference
  44. Kl Divergence
  45. Entropy
  46. Jensen's Inequality
  47. Gibb's Inequality
  48. Likelihood Principle
  49. Copy Editing
  50. Writing
  51. Writing Articles
  52. Presentations
  53. Writing Papers
  54. Writing Books
  55. Q-Learning
  56. Normalizing Flows
  57. Art
  58. Model-Based Reinforcement Learning
  59. Python
  60. Numpy
  61. Deep Reinforcement Learning
  62. Information Theoretic Reinforcement Learning
  63. Exploration In Reinforcement Learning
  64. Distributed Reinforcement Learning
  65. Transfer Learning
  66. Inverse Reinforcement Learning
  67. Human Behaviour As Optimal Control
  68. Control As Inference
  69. Hidden Markov Model
  70. Optimal Control and Planning
  71. Monte Carlo Tree Search
  72. Web Dev Tools
  73. Experience Replay
  74. Policy Gradients
  75. Reinforcement Learning
  76. CSS
  77. Actor-Critic
  78. Imitation Learning
  79. Generalized Value Functions
  80. Options Framework
  81. Game API Design
  82. API Design
  83. Code Litmus Tests
  84. Information Filter
  85. Simultaneous Localization and Mapping (SLAM)
  86. Web Development
  87. Meta Learning
  88. Generalization In RL
  89. Temporal Difference Learning
  90. Markov Decision Process
  91. Partially Observable Markov Decision Processes (POMDPs)
  92. Running
  93. Odometry Motion Model
  94. RSS Feeds
  95. LU Decomposition
  96. Google Cartographer
  97. Robotics
  98. Occupancy Grid Mapping
  99. Markov Localization
  100. Robot Localization
  101. Grid & Monte Carlo Localization
  102. EKF Localization
  103. State Estimation
  104. Likelihood Field Model
  105. Map Matching
  106. Range Finder Model
  107. Robot Kinematics
  108. Portfolio Composition
  109. Investment
  110. Investing In ETFs
  111. Velocity Motion Model
  112. Motion Model With Maps
  113. Robot Motion
  114. Non-parametric Filters
  115. Histogram Filter
  116. Bayes Filter
  117. Extended Kalman Filter
  118. Kalman Filter
  119. Gaussian Filter
  120. Arguments Against Zettelkasten
  121. Zettelkasten
  122. Markovian Assumption
  123. Robotics Probabilistic Generative Laws
  124. Uncertainty in Robotics
  125. Compilers
  126. Spiking Neurons
  127. GCC
  128. Robot Operating System (ROS)
  129. Sleep
  130. ARM Assembly Programming
  131. Statistical Methods for Finance
  132. Cognitive Hierarchy Model
  133. Model Compression
  134. Fitness
  135. Recommender Systems
  136. Datacouncil.ai Conference Notes
  137. Machine Teaching
  138. Hadoop
  139. XGBoost
  140. Scala
  141. Variational Inference
  142. Learning
  143. Rademacher Complexity
  144. Markov Chains
  145. Computer Networking
  146. Expectation Maximization and Mixture Models
  147. Machine Learning
  148. Probabilistic Graph Models
  149. VC-Dimension
  150. Regression
  151. Hopfield Network
  152. Ising Models
  153. The Bias-Complexity Tradeoff
  154. PAC Learning
  155. C++
  156. Hacking
  157. Probability Theory
  158. Statistics
  159. Databases
  160. CMake
  161. Formulation
  162. The C Language
  163. Haskell
  164. OCaml
  165. Studying
  166. Statistical Learning
  167. Stochastic Processes
  168. Random Variables
  169. Artificial Intelligence
  170. Optimization
  171. Linear Algebra
  172. Systems Programming
  173. Natural Language Processing
  174. Computer Vision
  175. Deep Learning Tools
  176. Theory Of Computation
  177. Negotiation
  178. BitTorrent
  179. Cryptography
  180. Data Science
  181. Spark
  182. Software Engineering
  183. Operating Systems
  184. Data Visualization
  185. Data Structures and Algorithms
  186. Programming Methodology
  187. Blockchain
  188. Deep Learning
  189. Computer Organization
  190. Google Cloud Platform
  191. HTTP
  192. Singapore Society
  193. Trigger List
  194. IS1103: Computing and Society
  195. Consciousness
  196. Podcasts
  197. Security
  198. Ideas
  199. Critical Thinking
  200. Weekly Review
  201. Conversation
  202. Dev Ops
  203. Java
  204. Productivity
  205. Daily Ritual
  206. Nix/NixOS
  207. Quantitative Reasoning
  208. System Design
  209. Topic Modeling
  210. Unix
  211. General Links
  212. React
  213. iOS
  214. Docker 101
  215. Coding Interview Preparation

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