JamesNewton

/*
PinReadWrite.ino
//20170512 initial version
//20170517 uS timing (was mS), vars are longs, i2C start/stop, and clock IN data w/.
//20201002 Returns valid JSON.

Simple Arduino script to set pins high, low, input, pull up, or analog/servo, 
clock out data with timing, and read all or a single pin back via serial IO. 
Written for the tiny-circuits.com TinyDuino in the end effector of the 
Dexter robot from HDRobotic.com, but generally useful to turn the Arduino

frfahim

How to install virtualenv:

Install pip first

sudo apt-get install python3-pip

Then install virtualenv using pip3

sudo pip3 install virtualenv 

dusty-nv

#!/bin/bash
#
# EDIT:  this script is outdated, please see https://forums.developer.nvidia.com/t/pytorch-for-jetson-nano-version-1-6-0-now-available
#
sudo apt-get install python-pip

# upgrade pip
pip install -U pip
pip --version
# pip 9.0.1 from /home/ubuntu/.local/lib/python2.7/site-packages (python 2.7)

keithweaver

# For more info: http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_gui/py_video_display/py_video_display.html
import cv2
import numpy as np

# Playing video from file:
# cap = cv2.VideoCapture('vtest.avi')
# Capturing video from webcam:
cap = cv2.VideoCapture(0)

currentFrame = 0

Integralist

import socket

hostname, sld, tld, port = 'www', 'integralist', 'co.uk', 80
target = '{}.{}.{}'.format(hostname, sld, tld)

# create an ipv4 (AF_INET) socket object using the tcp protocol (SOCK_STREAM)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

# connect the client
# client.connect((target, port))

PhirePhly

#!/usr/bin/env python

import paho.mqtt.client as mqtt
import RPi.GPIO as GPIO

def on_connect(client, userdata, rc):
    #print ("Connected with rc: " + str(rc))
    client.subscribe("kwf/demo/led")

def on_message(client, userdata, msg):

oliv2915

'''
Author: Brian Oliver II
Instagram: bolo_ne3
License:
    MIT License

    Copyright (c) 2016 Brian Oliver II

    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal

arundasan91

Freshly brewed !

With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

erikbern

# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

shweta-nerake1

#!/usr/bin/python3

#required libraries
import sys                                 
import ssl
import paho.mqtt.client as mqtt

#called while client tries to establish connection with the server 
def on_connect(mqttc, obj, flags, rc):
    if rc==0: