Brainiarc7

# first get the PPA repository driver
sudo add-apt-repository ppa:graphics-drivers/ppa

# install nvidai driver 
sudo apt install nvidia-384 nvidia-384-dev

# install other import packages
sudo apt-get install g++ freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libglu1-mesa libglu1-mesa-dev

# CUDA 9 requires gcc 6

emadehsan

Install Screen

$ sudo apt install screen

Enter a new Screen Session

$ screen

Detach from current screen session

faniska

# Uncomment the next line if you have installed wkhtmltopdf
# sudo apt remove wkhtmltopdf
cd ~
# Select an appropriate link for your system (32 or 64 bit) from the page https://wkhtmltopdf.org/downloads.html and past to the next line
wget https://github.com/wkhtmltopdf/wkhtmltopdf/releases/download/0.12.4/wkhtmltox-0.12.4_linux-generic-amd64.tar.xz
tar xvf wkhtmltox*.tar.xz
sudo mv wkhtmltox/bin/wkhtmlto* /usr/bin
sudo apt-get install -y openssl build-essential libssl-dev libxrender-dev git-core libx11-dev libxext-dev libfontconfig1-dev libfreetype6-dev fontconfig

khornberg

"""
Given a dictionary, transform it to a string. Then byte encode that string. Then base64 encode it and since this will go
on a url, use the urlsafe version. Then decode the byte string so that it can be else where.
"""
data = base64.urlsafe_b64encode(json.dumps({'a': 123}).encode()).decode()

# And the decode is just as simple...
data = json.loads(base64.urlsafe_b64decode(query_param.encode()).decode())
# Byte encode the string, base64 decode that, then byte decode, finally transform it to a dictionary

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.

makmac213

import json
import requests

from bottle import debug, request, route, run

GRAPH_URL = "https://graph.facebook.com/v2.6"
VERIFY_TOKEN = 'YOUR_VERIFY_TOKEN'
PAGE_TOKEN = 'YOUR_PAGE_TOKEN'

def send_to_messenger(ctx):

orenitamar

# Below are the dependencies required for installing the common combination of numpy, scipy, pandas and matplotlib 
# in an Alpine based Docker image.
FROM alpine:3.4
RUN echo "http://dl-8.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories
RUN apk --no-cache --update-cache add gcc gfortran python python-dev py-pip build-base wget freetype-dev libpng-dev openblas-dev
RUN ln -s /usr/include/locale.h /usr/include/xlocale.h
RUN pip install numpy scipy pandas matplotlib

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

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):

miguelmota

i3 Window Manager Cheat Sheet

$mod refers to the modifier key (window/command or alt by default depending on config)

General

• startx i3 start i3 from command line
• $mod+<Enter> open a terminal
• $mod+d open dmenu (text based program launcher)
• $mod+r resize mode ( or to leave resize mode)
• $mod+shift+e exit i3