Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
// original from: http://mashe.hawksey.info/2014/07/google-sheets-as-a-database-insert-with-apps-script-using-postget-methods-with-ajax-example/ | |
// original gist: https://gist.github.com/willpatera/ee41ae374d3c9839c2d6 | |
// NOTE: Uses es5 javascript | |
// handle method: get | |
function doGet(e){ | |
return handleResponse(e); | |
} | |
// handles method: post | |
function doPost(e){ |
This gist compares the performance of Julia, Nim, C++ and R - the latter using either POMP, or LibBi in a simple simulation of an SIR epidemiological model. In addition to keeping track of susceptibles, infecteds and recovereds, I also store the cumulative number of infections. Time moves in discrete steps, and the algorithm avoids language-specific syntax features to make the comparison as fair as possible, including using the same algorithm for generating binomial random numbers and the same random number generator; the exception are the R versions, POMP uses the standard R Mersenne Twister for the random number generator; I'm not sure what LibBi uses. The algorithm for generating random binomial numbers is only really suitable for small np.
Benchmarks were run on a Mac Pro (Late 2013), with 3 Ghz 8-core Intel Xeon E3, 64GB 1866 Mhz RAM, running OSX v 10.11.3 (El Capitan
package main | |
import ( | |
"fmt" | |
"math" | |
"time" | |
) | |
/* | |
Three people are playing the following betting game. |
Principles of Adult Behavior
You must use the magic method %save
:
In [1]: %save?
Type: Magic function
String Form:<bound method CodeMagics.save of <IPython.core.magics.code.CodeMagics object at 0x7fb5d25bb1d0>>
Namespace: IPython internal
File: /usr/lib/python2.7/dist-packages/IPython/core/magics/code.py
""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
/* | |
* I add this to html files generated with pandoc. | |
*/ | |
html { | |
font-size: 100%; | |
overflow-y: scroll; | |
-webkit-text-size-adjust: 100%; | |
-ms-text-size-adjust: 100%; | |
} |
#!/bin/bash | |
# bash generate random alphanumeric string | |
# | |
# bash generate random 32 character alphanumeric string (upper and lowercase) and | |
NEW_UUID=$(cat /dev/urandom | tr -dc 'a-zA-Z0-9' | fold -w 32 | head -n 1) | |
# bash generate random 32 character alphanumeric string (lowercase only) | |
cat /dev/urandom | tr -dc 'a-z0-9' | fold -w 32 | head -n 1 |