bpesquet

Bitmap bmp;
        Random rd = new Random();
        double xc1 = 200;
        double yc1 = 200;
        double rayon1 = 150;
        double sigma1 = 5.0;

        double xc2 = 350;
        double yc2 = 220;
        double rayon2 = 150;

bpesquet

export type Year = "1A" | "2A" | "3A";

export interface Module {
  id: string;
  name: string;
  description: string;
  teacher: string;
  year: Year;
  imageUrl: string;
}

bpesquet

export interface User {
  mail: string;
  password: string;
  nickName: string;
}

class UserService {
  users: Array<User> = [
    { mail: "test@test.fr", password: "test", nickName: "test" },
    { mail: "admin@test.fr", password: "admin", nickName: "admin" },

bpesquet

from micrograd.nn import Neuron, Layer, MLP

model = MLP(2, [16, 16, 1])  # 2-layer neural network

for k in range(100):
    # forward
    total_loss, acc = loss()
    
    # backward
    model.zero_grad()

bpesquet

from micrograd.engine import Value

# Create a managed scalar value
x = Value(-4.0)
z = 2 * x + 2 + x  # z = -10
q = z.relu() + z * x  # q = 40
h = (z * z).relu()  # h = 100
y = h + q + q * x  # y = -20

# Compute gradients w.r.t. input values

bpesquet

# update all packages unprompted
conda update --all -y

# list packages that can be updated
conda search --outdated

bpesquet

{
  "extends": ["airbnb", "prettier", "prettier/react"],
  "parser": "babel-eslint",
  "env": {
    "react-native/react-native": true
  },
  "plugins": ["react", "react-native"],
  "parserOptions": {
    "ecmaFeatures": {
      "jsx": true

bpesquet

• Ubuntu 16.04
• CUDA 9.2
• cuDNN 7.1.4
• TF dependancies
• Anaconda 3 in /usr/local/anaconda3
• Clone TF 1.8
• Compil TF (Python 3.6 par défaut)
• Build package TF
• sudo visudo. Add /usr/local/anaconda3/bin at end of secure_path
• sudo pip install tensorflow*.whl

bpesquet

var Character = {};
Character.initCharacter = function (name, health, strength) {
    this.name = name;
    this.health = health;
    this.strength = strength;
};
Character.attack = function (target) {
    if (this.health > 0) {
        var damage = this.strength;
        console.log(this.name + " attacks " + target.name + " and deals " + damage + " damage");

bpesquet

$app->get('/hashpwd', function() use ($app) {
    $rawPassword = 'secret';
    $salt = '%qUgq3NAYfC1MKwrW?yevbE';
    $encoder = $app['security.encoder.bcrypt'];
    return $encoder->encodePassword($rawPassword, $salt);
});