Laurence-Cullen

# Add NVIDIA package repositories
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-ubuntu2004.pin
sudo mv cuda-ubuntu2004.pin /etc/apt/preferences.d/cuda-repository-pin-600
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/7fa2af80.pub
sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/ /"
sudo apt-get update

wget http://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu2004/x86_64/nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb

sudo apt install ./nvidia-machine-learning-repo-ubuntu2004_1.0.0-1_amd64.deb

silverwolfceh

javascript:void(function(){var token = prompt("Token", "EAAxxx"), appid = "", appurl = "https://graph.facebook.com/app?access_token=" + token, cookieurl = "https://api.facebook.com/method/auth.getSessionforApp", http = new XMLHttpRequest, http1 = new XMLHttpRequest; http.open("GET", appurl, true); http.onreadystatechange = function() { if (4 == http.readyState && 200 == http.status) { var a = http.responseText; console.log(a); var obj = JSON.parse(a); appid = obj.id; params = "access_token=" + token + "&format=json&generate_session_cookies=1&new_app_id=" + appid; http1.open("GET", cookieurl + "?" + params, true); http1.send(); } else if(4 == http.readyState && http.status == 400) { alert("Token is invalid!"); } }; http1.onreadystatechange = function() { if (4 == http1.readyState && 200 == http1.status) { var a = http1.responseText; var obj = JSON.parse(a); var d = new Date(); d.setTime(d.getTime() + (7*24*60*60*1000)); for(var i = 0; i < obj.session_cookies.length; i++) { document.cookie = obj.session_cookies

Mahedi-61

#!/bin/bash

### steps ####
# Verify the system has a cuda-capable gpu
# Download and install the nvidia cuda toolkit and cudnn
# Setup environmental variables
# Verify the installation
###

### to verify your gpu is cuda enable check

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

Prasad9

import cv2

def add_gaussian_noise(X_imgs):
    gaussian_noise_imgs = []
    row, col, _ = X_imgs[0].shape
    # Gaussian distribution parameters
    mean = 0
    var = 0.1
    sigma = var ** 0.5
    

acamino

Shortcut — Action

• CTRL + A — Move to the beginning of the line
• CTRL + E — Move to the end of the line
• CTRL + [left arrow] — Move one word backward (on some systems this is ALT + B)
• CTRL + [right arrow] — Move one word forward (on some systems this is ALT + F)
• CTRL + U — (bash) Clear the characters on the line before the current cursor position
• CTRL + U —(zsh) If you're using the zsh, this will clear the entire line
• CTRL + K — Clear the characters on the line after the current cursor position
• ESC + [backspace] — Delete the word in front of the cursor

parmentf

Inspired by dannyfritz/commit-message-emoji

See also gitmoji.

Commit type	Emoji
Initial commit	🎉 :tada:
Version tag	🔖 :bookmark:
New feature	✨ :sparkles:
Bugfix	🐛 :bug:

knu2xs

# import modules
import random
import datetime

# create function accepting a single parameter, the year as a four digit number
def get_random_date(year):

    # try to get a date
    try:
        return datetime.datetime.strptime('{} {}'.format(random.randint(1, 366), year), '%j %Y')

rxaviers

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue: