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Articole: 35
  1. Homemade Quetelet Rings

    Homemade Quetelet Rings Centers of other optical phenomena Why are there bands of color?

  2. Why Abandoned?

  3. False Sharing

    Sharing, True and False Cache Coherency and Cache Design a, b Example Performance M-series Processor Performance Atomic Operations False Sharing Mutexes Conditions for Performance Loss Conclusion Which program is faster?

  4. B+ trees are a fun test-bed to study memory performance because they combine sequential and random access patterns.

  5. Reasons to think gshare is better gshare is better gshare is worse Mathematical proof that gshare is worse Intuitive explanation why gshare is worse XOR Concatenation Conclusion This is part of my branch prediction series.

  6. Visualizing CPU Pipelining

    Visualizing pipelines Instruction Decoding Hazard detection Forwarding HDU and FU Branching Predict branch not taken Dynamic branch prediction Conclusion This is part of my branch prediction series.

  7. Linear Pathing Dijkstra’s Algorithm A* Search Algorithm A* Tricks Implicit Graph Data Structure Geometry Informed Heuristics See the discussion of this post on Hacker News.

  8. Separate Logical and Timing Requirements State Changes Debuggers MapReduce Raft This November I worked on MIT 6.824: Distributed Systems (the spring 2023 semester is called 6.5840).

  9. Crafting Interpreters is an awesome book by Robert Nystrom about interpreters, compilers, and programming languages.

  10. Windows Setup WSL iPad I recently got an iPad and I’d like to use it to write some code.

  11. Writing R tutorials with GPT-3

    dplyr Prompt Response dplyr - hadley Prompt Response Inspired by Gwern’s GPT-3 generated fiction and Github Copilot, I wanted to see if GPT-3 could write R tutorials.

  12. Introduction Why Gumshoe?

  13. How Game Mechanics Create Tension

    Introduction Encounter Model Decision Tension Risk Costs Conclusion Introduction Most advice about how to create tension in role-playing games is about the narrative: make things personal, raise the stakes, or create a sense of paranoia.1 But the rules of…

  14. Elementary School Addition Algorithm Hardware Complexity Elementary School Adder Carry-lookahead Adder Space Complexity Real-world Implementation Elementary School Addition Algorithm The elementary school addition algorithm works for binary numbers 1: 1 1…

  15. Introduction Components of time series Cosine components Time series dimensionality reduction How much reduction is possible?

  16. Introduction Distance Metrics Time Series Distance Dynamic Time Warping Note: This is a part of a series of articles for my package tsrecipes (Github, website).

  17. Introduction Concise lambdas with tab-complete Consistent output for common operations Unified group API Introduction If you find yourself doing data analysis in Python, you should check out siuba. siuba is a port of the R package dplyr, but you don’t need

  18. Siuba and IPython tab-complete

    Introduction Completion IPython and Completions Environment Commands Completion Appendix Siuba Why not hp?

  19. Introduction tidymodels and sklearn Introduction tidymodels is the new framework from Max Kuhn, David Vaughan, and Julia Silge at RStudio.

  20. Syntax Highlighting

    Themes and Parsing Parsing Themes Themes and Parsing There are two dimensions to syntax highlighting: parsing and themes.

  21. Introduction Pages (and sandwiches) Lists (and menus) Dinner is served Introduction Hugo Themes and blogdown make blogging as easy as writing markdown or RMarkdown, but in the back of my mind I’ve never been totally satisfied by the defaults.

  22. Reading for Pleasure

    Reading books serves many functions.

  23. Introduction How to use contrasts in R Cheatsheet Pairwise differences Comparison to overall mean Pairwise comparisons within groups Consecutive comparisons Before and after Introduction Inspired by Jonas K.

  24. dplyr 1.0.0

    Introduction Stop Repeating Names Summary Rows Introduction dplyr 1.0.0 is coming soon!

  25. AWS Sagemaker and R

    Introduction AWS Sagemaker AWS cost Productionizing machine learning predictions Sagemaker Features Training and Evaluation Deployment What Sagemaker isn’t sagemaker R package What to watch out for What’s next Introduction I’m excited to announce my sagem…

  26. funneljoin Gaps vs. deadlines How to model deadlines What I’d like to see Conclusion funneljoin In my previous blog post, I explored the awesome funneljoin package.

  27. Funnel Charts in R with funneljoin

    funneljoin Customer usage funnels Usage example Funnel types Gaps Conversions Conclusion funneljoin Emily Robinson has an awesome new package funneljoin. funneljoin has an intuitive interface to create and analyze funnels.

  28. Search Files in RStudio

    Introduction RStudio Command-line ack Searching File Names Github Introduction It’s been a while since I’ve posted, but I’ve been staying busy with some other projects that I’ll talk about soon.

  29. Introduction Typed Words and Variables Strings Expressions Functions Quosures Nested Functions Parsing Expressions Unknown Number of Columns Conclusions Introduction Hadley Wickham is creating awesome new resources for learning tidy eval: new Advanced R c…

  30. Introduction What I learned about R Packages S3, Tibble, and Pillar Non-Standard Evaluation What I learned about Machine Learning Ensembles Keras Conclusions Introduction I competed in my first Kaggle contest and I learned a lot.

  31. Embed Slides in Your Blog

    Introduction Embed in one line of code Specific slides Upload your slideshow to your website Future work Acknowledgements Introduction It’s rstudio::conf time and I’ve seen a lot of great presentations shared on twitter.

  32. Introduction git Setup Creating Updating and Version Control Code Externalization by Chunks Example 1: The Basics Example 2: External Data Conclusion Acknowledgements Introduction Here’s the scenario: I’m working on my project, using a git repo to track c…

  33. Tidying Messy Spreadsheets

    Introduction Why Tidy?

  34. MRP Using brms and tidybayes

    Introduction The Data Tidying Variables Tidying Poststratification Model 1: Disaggragation Model 2: Maximum Likelihood Multilevel Model Model 3: Full Bayesian Model Model Comparisons Introduction In the last post I wrote the “MRP Primer” Primer studying t…

  35. Introduction Another example The Total Statistic The Total and Weighted Averaging Conclusion Introduction This post will introduce poststartification: the process of correcting non-representative samples to better reflect the actual population proportions.