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  1. We are building an Ads ranking ML system from scratch.

  2. A minimal example project for running Large Language Model (LLM) inference on CHTC GPU Lab at UW-Madison.

  3. I knew about this event only three days before it occurred.

  4. I really enjoyed my experiences at MEB 2025 .

  5. First, find the top largest files find ~ -type f -exec du -h {} + | sort -rh | head -n 20 Clean conda: conda clean -a -y, this will remove Tarballs and extracted packages, Index caches, and Logs Conda envs: List all envs: conda info --envs Remove one: con…

  6. Matthew Conlen explains KDE well with a dynamic visualization , worth referring to.

  7. HT Condor Tricks

    I have been using HT Condor (through UW Madison’s CHTC) a lot.

  8. Explanining Log-Sum-Exp

    Motivationn & Definition In this post, we explain the concept of Log-Sum-Exp .

  9. Concept Explanation The Infinite Gaussian Mixture Model (IGMM) is an extension of the Gaussian Mixture Model .

  10. How do you plan to contribute to the field of Computer Science or Statistics in your future career?

  11. Bayesian Statistics

    An introduction to Bayesian thinking Think Bayes 2 Probabilistic Programming & Bayesian Methods for Hackers

  12. I saw this post on Stackoverflow.

  13. The first step is to create the context, for example, UsernameContext.js: import { createContext } from 'react'; const UsernameContext = createContext(""); export default UsernameContext; The second step is (1) initiate the context values and (2) use the

  14. There are two ways to create a form in React.

  15. Live presentation (starts from 14:00) Fast forward Prerecorded Talk

  16. Images in this posst came from the slides of CS524 at UW-Madison , 2023Spring This notebook runs in Julia and is rendered by Hupyter .

  17. Images in this posst came from the slides of CS524 at UW-Madison , 2023Spring This notebook runs in Julia and is rendered by Hupyter .

  18. Images in this posst came from the slides of CS524 at UW-Madison , 2023Spring This notebook runs in Julia and is rendered by Hupyter .

  19. Images in this posst came from the slides of CS524 at UW-Madison , 2023Spring This notebook runs in Julia and is rendered by Hupyter .

  20. Images in this posst came from the slides of CS524 at UW-Madison , 2023Spring This notebook runs in Julia and is rendered by Hupyter .

  21. Images in this posst came from the slides of CS524 at UW-Madison , 2023Spring This notebook runs in Julia and is rendered by Hupyter .

  22. Notes taken on Professor Xiaopeng Li ’s class of CIV ENGR 679 Connected and Automated Transport, 2023Spring level 1: driver assistance.

  23. Three are three requirements for passing the qual at UW CS: The committee should comprise of at least three faculty members.

  24. Matrix `$$ A = \begin{bmatrix} 1 & -1 & 0 & 0 \\ 0 & 0 & 1 & -1 \\ -1 & 1 & 0 & 0 \\ 0 & 0 & -1 & 1 \end{bmatrix} $$` $$ A = \begin{bmatrix} 1 & -1 & 0 & 0 \\ 0 & 0 & 1 & -1 \\ -1 & 1 & 0 & 0 \\ 0 & 0 & -1 & 1 \end{bmatrix} $$

  25. The material below is based on Professor Xiaopeng Li ’s course of CIV ENGR 679 Connected and Automated Transport This notebook runs in Julia and is rendered by Hupyter .

  26. The material below is based on Professor Xiaopeng Li ’s course of CIV ENGR 679 Connected and Automated Transport This notebook runs in Julia and is rendered by Hupyter .

  27. I was not able to fully master the directory specification in .gitignore but I do not have to.

  28. Through this tutorial, I want to explain how to compuate the multiplications of non-square matrix; for example, the result of multiplying a $2 \times 5$ matrix by a $5 \times 2$ matrix (a by c in the following example.) import numpy as np a = np.array([[1,

  29. What is the derivative of $f(x) = log(x)$?

  30. This blog post is inspired by MLDawn’s video on the same topic.

  31. Given that we have the sigmoid function: $$\sigma(x) = \frac{1}{1 + e^{-x}}$$ What is its derivative?

  32. import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import altair as alt Download data here: aiddata.csv Data import and basic manipulation df = pd.read_csv('..

  33. Lesson 1: Preview Think about the area of a circile.

  34. To me, K Nearest Neighbors (KNN) is very intuitive.

  35. This is how you update a value at a specific cell: Suppose the row index of the cell is row_idx, and the column label is 'colname', then you can update the cell value this way: df.at[row_idx, 'colname'] = new_value.

  36. I attended the writing group led by YY on September 22, 2022 where YY, Vincent Wong, and I attended.

  37. Rolling a die Imagine you have a 4-sided fair die, with numbers of ‘1’, ‘2’, ‘3’, and ‘4’.

  38. What is knowledge?

  39. 1.

  40. Please refer to the documentation of hupyter repo .

  41. Suppose we have: $$\vec{a} = \begin{bmatrix} a_x \\ a_y \end{bmatrix}$$ $$\vec{b} = \begin{bmatrix} b_x \\ b_y \end{bmatrix}$$ I am wondering why $\vec{a} \cdot \vec{b} = a_x b_x + a_y b_y$.

  42. The other day I was thinking about the meaning of standard deviation: $$\sigma = \frac{\sum_{i=1}^{N}(x_i - \bar{x})}{N-1}$$ What is it about?

  43. In this post, I’ll explain what hierarchical clustering is and how to implement it with Python. import pandas as pd import numpy as np import matplotlib.pyplot as plt np.random.seed(1234) N = 10 # number of points matrix = np.random.rand(N, 2) # N points…

  44. Everyone seems to know correlation but they do not.

  45. Re-Understanding Z Score

    Ask anyone who has attended Stats101 and s/he will tell you that they understand Z-score.

  46. y <- function(x) { x^2 } curve(y, from=1, to=50, xlab='x', ylab='y') y <- function(x) {2^x} curve(y, from=1, to=10, xlab="x", ylab="y") The above codes are from https://stackoverflow.com/a/26091375.

  47. I just learned this several days ago: set in python is unsorted and changes from time to time.

  48. The plots were not showing in a hugo post by Blogdown.

  49. I found the output of Inscape is not optimal. svg2pdf.js is much better!

  50. To put year in parenthesis when citing in LaTex using APA format, for exmaple, According to Goodguy (1990), blablabla..., we can use this: \newcommand{\citeauthorandyear}[2][]{ \citeauthor{#2} (\citeyear[#1]{#2}) } This solution is by Felix K. .