Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
![Screenshot 2023-12-18 at 10 40 27 PM](https://private-user-images.githubusercontent.com/3837836/291468646-4c30ad72-76ee-4939-a5fb-16b570d38cf2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.IfotNP5Z15K6UJlaH0OGw0bSWYbGDcDF15gfhLQ2ZTE)
Twitter thread: https://twitter.com/theshawwn/status/1456925974919004165
Hacker News thread: https://news.ycombinator.com/item?id=29128998
November 6, 2021
jnp.device_put(1)
is deceptively simple to write in JAX. But on a TPU, what actually happens? How does a tensor containing the value 1
actually get onto a TPU?
Turns out, the answer is "C++", and a lot of it.
site: https://tamuhey.github.io/tokenizations/
Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:
// ==UserScript== | |
// @name View Image | |
// @namespace https://github.com/bijij/ViewImage | |
// @version 4.1.1 | |
// @description This userscript re-implements the "View Image" and "Search by image" buttons into google images. | |
// @author Joshua B | |
// @run-at document-end | |
// @include http*://*.google.tld/search*tbm=isch* | |
// @include http*://*.google.tld/imgres* | |
// @updateURL https://gist.githubusercontent.com/bijij/58cc8cfc859331e4cf80210528a7b255/raw/viewimage.user.js |
####Rets Rabbit http://www.retsrabbit.com
Rets Rabbit removes the nightmare of importing thousands of real estate listings and photos from RETS or ListHub and gives you an easy to use import and Web API server so you can focus on building your listing search powered website or app.
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
package main | |
import ( | |
"log" | |
"bufio" | |
"time" | |
"os" | |
"fmt" | |
"io" | |
"net" |
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
// Apple Crayon Palette RGB | |
$cantaloupe: rgb(255, 206, 110); | |
$honeydew: rgb(206, 250, 110); | |
$spindrift: rgb(104, 251, 208); | |
$sky: rgb(106, 207, 255); | |
$lavender: rgb(210, 120, 255); | |
$carnation: rgb(255, 127, 211); | |
$licorice: rgb(0, 0, 0); | |
$snow: rgb(255, 255, 255); |
# Bash function gen_random_filename | |
# Description: Generates random file names | |
# Requires shuf (brew install coreutils) | |
# Requires a list of adjectives and nouns (1 per line) | |
gen_random_filename() { | |
local adjs=~/words/adjectives.txt | |
local nouns=~/words/nouns.txt | |
local adj noun title starts_with_1 starts_with_2 counter |