bitsnaps

# Install Docker 20.10.7 on Ubuntu 20.04 focal
sudo apt-get update

sudo apt-get install apt-transport-https ca-certificates curl gnupg lsb-release -y

curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg

# This is for amd64_x86 architecture (checkout for other architectures: https://docs.docker.com/engine/install/ubuntu/) 
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu \
  $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

bitsnaps

// Dart is a C-Like strongly static typed language
// variable are pass-reference-by-value

// Everything is an Object

/*
 * Data Type in Dart
Data Type	| Keyword	          | Description
Number    |	int, double, num	| Numbers in Dart are used to represent numeric literals
Strings	  | String	          | Strings represent a sequence of characters

bitsnaps

// We know we can apply type checking and compile static on all groovy classes, the good news you can do this on gradle build, so it'll be applied in every class in your project, this works in android project.

/*/ In gradle project:
apply plugin: 'groovy'
compileGroovy.groovyOptions.configurationScript = file('gradle/config/groovyc.groovy')

// In Android project (app/build.gradle)
androidGroovy {
    options {
        configure(groovyOptions) {

bitsnaps

# Simplest linear model with keras 2.1.3 (using tensorflow backed) it worked with python 3.5
import numpy as np
import keras

model = keras.Sequential( [keras.layers.Dense(units=1, input_shape=[1])] )
model.compile(optimizer='sgd', loss='mean_squared_error')

# y = 2x - 1
xs = np.array([-1.0, 0.0, 1.0, 2.0, 3.0, 4.0])
ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0])

bitsnaps

<template>
  <div id="app">
    <!-- <img src="./assets/logo.png">
    <HelloWorld msg="Welcome to Your Vue.js App"/> -->
    <div class="reveal">
      <div class="slides">
        <section>Single Horizontal Slide</section>
        <section>
          <section>Vertical Slide 1</section>
          <section>Vertical Slide 2</section>

bitsnaps

Transfer a gist to a GitHub repository

clone the gist

git clone https://gist.github.com/ishu3101/6fb35afd237e42ef25f9

rename the directory

mv 6fb35afd237e42ef25f9 ConvertTo-Markdown

change the working directory to the newly renamed directory

cd ConvertTo-Markdown

bitsnaps

package demo

import javax.script.*
import org.renjin.script.*

class App {
  static void main(String[] args){
    // init values
    int[] values = [1,2,3]
    // Create a Renjin engine:

bitsnaps

"""
Update: (07.29.2022): Fix: train_test_split import
==============================================================
Restricted Boltzmann Machine features for digit classification
==============================================================

For greyscale image data where pixel values can be interpreted as degrees of
blackness on a white background, like handwritten digit recognition, the
Bernoulli Restricted Boltzmann machine model (:class:`BernoulliRBM
<sklearn.neural_network.BernoulliRBM>`) can perform effective non-linear

bitsnaps

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd 
import math

class UpperConfidenceBound:
  def __init__(self, dataframe,  N, m):
    self.__dataset = dataframe
    self.__N = N
    self.__m = m

bitsnaps

def text = "Going to convert this to Base64 encoding!"

// Encode
def encoded = text.bytes.encodeBase64().toString()
println encoded


// Decode
byte[] decoded = encoded.decodeBase64()
println new String(decoded)