#Comprehensive Introduction to @ngrx/store By: @BTroncone
Also check out my lesson @ngrx/store in 10 minutes on egghead.io!
Update: Non-middleware examples have been updated to ngrx/store v2. More coming soon!
{ | |
"@context": { | |
"@base": "file:///hue-light", | |
"@vocab": "file:///hue-light#", | |
"iot": "https://iotdb.org/pub/iot#", | |
"iot-attribute": "https://iotdb.org/pub/iot-attribute#", | |
"schema": "http://schema.org/" | |
}, | |
"@id": "", | |
"@type": "iot:Model", |
#Comprehensive Introduction to @ngrx/store By: @BTroncone
Also check out my lesson @ngrx/store in 10 minutes on egghead.io!
Update: Non-middleware examples have been updated to ngrx/store v2. More coming soon!
license: gpl-3.0 |
#!/bin/bash | |
function install_build_tools { | |
sudo apt-get install git | |
sudo apt-get install libasound2-dev | |
sudo apt-get install build-essential | |
sudo apt-get install make | |
sudo apt-get install autoconf | |
sudo apt-get install libtool |
For best results, you should read through the official NVIDIA documentation found on:
https://developer.nvidia.com/linux-tegra-rel-21
In particular, the Quick Start Guide.
For this process you will need:
#!/usr/bin/env bash | |
# Installation script for Deep Learning Libraries on Ubuntu 14.04, by Roelof Pieters (@graphific) | |
# BSD License | |
orig_executor="$(whoami)" | |
if [ "$(whoami)" == "root" ]; then | |
echo "running as root, please run as user you want to have stuff installed as" | |
exit 1 | |
fi | |
################################### |
bool ReadCSVToDatum(const string& filename, Datum* datum) { | |
// read in the CSV file into a vector | |
fstream file(filename.c_str(), ios::in); | |
vector<vector<int> > label; | |
std::string line; | |
while (std::getline(file, line)) { | |
// replace commas with spaces | |
for (int i = 0; i < line.length(); i++) { | |
if (line[i] == ',') | |
line[i] = ' '; |
from __future__ import division | |
import cv2 | |
import cv2.cv as cv | |
import sys | |
import pdb | |
def detect(img, cascade): | |
rects = cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=3, minSize=(10, 10), flags = cv.CV_HAAR_SCALE_IMAGE) | |
if len(rects) == 0: | |
return [] |