hubgit

<?xml version="1.0" encoding="UTF-8"?>
<config>
  <indexDefaults>
    <useCompoundFile>false</useCompoundFile>
    <mergeFactor>10</mergeFactor>
    <ramBufferSizeMB>256</ramBufferSizeMB>
    <maxFieldLength>100000000</maxFieldLength>
    <writeLockTimeout>1000</writeLockTimeout>
    <commitLockTimeout>10000</commitLockTimeout>
    <lockType>native</lockType>

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

yoavram

import requests

#http://docs.python-requests.org/en/latest/user/quickstart/#post-a-multipart-encoded-file

url = "http://localhost:5000/"
fin = open('simple_table.pdf', 'rb')
files = {'file': fin}
try:
  r = requests.post(url, files=files)
	print r.text

d2s

Installing Node.js with nvm to Linux & macOS & WSL

A quick guide on how to setup Node.js development environment.

Install nvm for managing Node.js versions

nvm allows installing several versions of Node.js to the same system. Sometimes applications require a certain versions of Node.js to work. Having the flexibility of using specific versions can help.

1. Open new Terminal window.

subfuzion

Common Options

-#, --progress-bar Make curl display a simple progress bar instead of the more informational standard meter.

-b, --cookie <name=data> Supply cookie with request. If no =, then specifies the cookie file to use (see -c).

-c, --cookie-jar <file name> File to save response cookies to.

wassname

"""
Here is a dice loss for keras which is smoothed to approximate a linear (L1) loss.
It ranges from 1 to 0 (no error), and returns results similar to binary crossentropy
"""

# define custom loss and metric functions 

from keras import backend as K

def dice_coef(y_true, y_pred, smooth=1):

bxshi

#!/bin/bash

# Script for installing tmux on systems where you don't have root access.
# tmux will be installed in $INSTALL_DIR/local/bin.
# It's assumed that wget and a C/C++ compiler are installed.

# exit on error
set -e

TMUX_VERSION=2.3

wassname

from keras import backend as K

def jaccard_distance_loss(y_true, y_pred, smooth=100):
    """
    Jaccard = (|X & Y|)/ (|X|+ |Y| - |X & Y|)
            = sum(|A*B|)/(sum(|A|)+sum(|B|)-sum(|A*B|))
    
    The jaccard distance loss is usefull for unbalanced datasets. This has been
    shifted so it converges on 0 and is smoothed to avoid exploding or disapearing
    gradient.

iandanforth

%%javascript
// Source - https://stackoverflow.com/a/23395136

Jupyter.beep = () => {
    var snd = new Audio("data:audio/wav;base64,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