| Homebrew build logs for neovim on macOS 14.1 | |
| Build date: 2023-12-17 22:10:16 |
| Homebrew build logs for neovim on macOS 14.1 | |
| Build date: 2023-12-17 22:10:16 |
See this tweet for how I use this.
If it doesn't already exist, create a folder called chrome in your profile folder. The easiest way to find your profile folder is in about:profiles (there's a "Show in Finder" link for the folder on macOS). Be sure to choose the right profile is you have more than one.
Put userContent.css above in the chrome folder you just created.
| <!doctype html> | |
| <html> | |
| <head> | |
| <title>Joy of Computing</title> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width initial-scale=1 maximum-scale=1 user-scalable=no"> | |
| <link rel="stylesheet" media="screen" href="https://d29xw0ra2h4o4u.cloudfront.net/assets/joy_of_computing-3384bb049269a50b6518dc7e97c534fa1557323722ef3dc26d23b1d0b54048f3.css" /> | |
| <script src="https://d29xw0ra2h4o4u.cloudfront.net/assets/joy_of_computing-9a4831474100e94089cc86a7d5487efe27894358edcf5b63ce8eb5b126643f9b.js"></script> | |
| <meta name="csrf-param" content="authenticity_token" /> |
| # !wget http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip | |
| # !unzip trainingandtestdata.zip | |
| # !pip3 install joblib sklearn | |
| import pandas as pd | |
| import joblib | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.feature_selection import SelectKBest, chi2 |
https://www.youtube.com/channel/UCrJhliKNQ8g0qoE_zvL8eVg/feed
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