hwchase17

# STEP 1: Load

# Load documents using LangChain's DocumentLoaders
# This is from https://langchain.readthedocs.io/en/latest/modules/document_loaders/examples/csv.html

from langchain.document_loaders.csv_loader import CSVLoader
loader = CSVLoader(file_path='./example_data/mlb_teams_2012.csv')
data = loader.load()

tomhicks

/*
Copy this into the console of any web page that is interactive and doesn't
do hard reloads. You will hear your DOM changes as different pitches of
audio.

I have found this interesting for debugging, but also fun to hear web pages
render like UIs do in movies.
*/

const audioCtx = new (window.AudioContext || window.webkitAudioContext)()

tomasklapka

#!/bin/bash

BITS=2048
DIR=./out

if grep -q 'webid_generator' /etc/ssl/openssl.cnf; then
    echo "WEBID_GEN: Found webid_generator configuration section in /etc/ssl/openssl.cnf"
else
    echo "WEBID_GEN: Section webid_generator is missing in /etc/ssl/openssl.cnf. Add this configuration section manually:

rosskevin

// @flow

declare module 'react-relay' {
  declare export type RecordState = 'EXISTENT' | 'NONEXISTENT' | 'UNKNOWN';

  declare export type onCompleted = (response: ?Object, errors: ?Array<PayloadError>) => void
  declare export type onError = (error: Error) => void

  declare export type CommitOptions = {
    onCompleted: onCompleted,

Dref360

import operator
import threading
from functools import reduce

import keras
import keras.backend as K
from keras.engine import Model
import numpy as np
import tensorflow as tf
import time

awjuliani

aoxu

/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
brew install privoxy
vim /usr/local/etc/privoxy/config
forward-socks5 / 127.0.0.1:1080 .
brew services start privoxy
export https_proxy=http://127.0.0.1:8118;export http_proxy=http://127.0.0.1:8118

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

nzw0301

*2vec papers

1. act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
2. apk2vec https://arxiv.org/abs/1809.05693
3. app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
4. ast2vec https://arxiv.org/abs/2103.11614
5. attribute2vec https://arxiv.org/abs/2004.01375
6. author2vec http://dl.acm.org/citation.cfm?id=2889382
7. baller2vec https://arxiv.org/abs/2102.03291
8. bb2vec https://arxiv.org/abs/1809.09621

gabrieleangeletti

import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):