learncodeacademy

import * as tf from "@tensorflow/tfjs"
import "@tensorflow/tfjs-node"
import iris from "./iris.json"
import irisTesting from "./iris-testing.json"

// convert/setup our data
const trainingData = tf.tensor2d(iris.map(item => [
  item.sepal_length, item.sepal_width, item.petal_length, item.petal_width,
]))
const outputData = tf.tensor2d(iris.map(item => [

learncodeacademy

Start Training!...Here's the data, we'll do 2 iterations:
[ { input: [ 0, 1 ], output: [ 1, 0 ] },
  { input: [ 1, 1 ], output: [ 1, 1 ] } ]
======== TRAINING ITERATION 1 =========
--------- Run input set 0: 0,1 ----------
-> Layer 2 has 3 nodes
   START NODE: 0
   -> bias for node 0: 0.13861538469791412
   -> weights for node 0:
     -> input value: 0, weight: -0.03485306352376938

learncodeacademy

pipeline:
  slack:
    image: plugins/slack
    webhook: <your slack webhook url>
    channel: deployments
    username: drone
    template: "<{{build.link}}|Deployment #{{build.number}} started> on <http://github.com/{{repo.owner}}/{{repo.name}}/tree/{{build.branch}}|{{repo.name}}:{{build.branch}}> by {{build.author}}"
    when:
      branch: [ master, staging ]
  build:

learncodeacademy

const axios = require("axios");
const key = "AIzaSyB9e-dHIvdxxrbmorjYHWipwBKq7LJBhNk"

function getComments(pageToken, allItems = {}) {
  const params = {
    key,
    videoId: "a4haLJdDRmc",
    part: "snippet",
    maxResults: 100,
    pageToken,

learncodeacademy

#include "ergodox_ez.h"
#include "debug.h"
#include "action_layer.h"

#define BASE  0 // default layer
#define MDIA  1 // mouse/media keys
#define ARROW 2 // arrow keys

const uint16_t PROGMEM keymaps[][MATRIX_ROWS][MATRIX_COLS] = {
/* Keymap 0: Basic layer

learncodeacademy

var debug = process.env.NODE_ENV !== "production";
var webpack = require('webpack');

module.exports = {
  context: __dirname,
  devtool: debug ? "inline-sourcemap" : null,
  entry: "./js/scripts.js",
  output: {
    path: __dirname + "/js",
    filename: "scripts.min.js"

learncodeacademy

Running a High Availability Service on CoreOS using Docker, Fleet, Flannel, Etcd, Confd & Nginx

• View the diagram of what's going on: https://drive.google.com/file/d/0B74l6vUHf9HrMzNrYUlyMm40RWs/view?usp=sharing

Tools used:

• coreos: server machine clustering via a shared cloud-config.yml
• etcd: key value store for service registration and discovery
• fleet: scheduling/failover of docker containers across coreos cluster
• flannel: Gives each docker container a unique ip that allows you to access the internal port (i.e. port 80 not 32679)
• confd: watch etcd for nodes arriving/leaving - template nginx configuration files / reload nginx on change

learncodeacademy

//events - a super-basic Javascript (publish subscribe) pattern

var events = {
  events: {},
  on: function (eventName, fn) {
    this.events[eventName] = this.events[eventName] || [];
    this.events[eventName].push(fn);
  },
  off: function(eventName, fn) {
    if (this.events[eventName]) {

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FROM node:0.12.0

# use changes to package.json to force Docker not to use the cache
# when we change our application's nodejs dependencies:
ADD package.json /tmp/package.json
RUN cd /tmp && mkdir data && npm install --production --unsafe-perm
RUN mkdir -p /app && cp -a /tmp/node_modules /app/ && cp -a /tmp/data /app/

# From here we load our application's code in, therefore the previous docker
# "layer" thats been cached will be used if possible

learncodeacademy

###Prerequesites

• Install node.js from http://nodejs.org
• Make sure you install git from http://git-scm.com

Install flightplan globally

npm install -g flightplan

Install flightplan in your project folder