pahud

import { App, Construct, Stack, StackProps, CfnParameter, CfnCondition, Fn } from '@aws-cdk/core';
import * as sns from '@aws-cdk/aws-sns';

export interface DemoProps { }

export class Demo extends Construct {
  constructor(scope: Construct, id: string) {
    super(scope, id)

    const stack = Stack.of(this);

cajames

import cdk = require("@aws-cdk/core");
import { Vpc, Port } from "@aws-cdk/aws-ec2";
import {
  Cluster,
  ContainerImage,
  AwsLogDriver,
  FargatePlatformVersion,
  NetworkMode,
  CfnService,
} from "@aws-cdk/aws-ecs";

codebrainz

/*
* one.c -- Lua core, libraries, and interpreter in a single file (Lua 5.3)
*/

/* default is to build the full interpreter */
#ifndef MAKE_LIB
#ifndef MAKE_LUAC
#ifndef MAKE_LUA
#define MAKE_LUA
#endif

tsu-nera

import gym
import numpy as np
from collections import deque
from gym import wrappers

# Create the Cart-Pole game environment
env = gym.make('CartPole-v0')
env = wrappers.Monitor(env, '/tmp/cartpole-experiment-1')

def relu(x):

StanislawAntol

#!/usr/bin/python3

from sys import exit
from sys import stdout
from sys import path as syspath
from os import path as osp
from os import stat, makedirs
import argparse

from shutil import copyfile

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

aaronpolhamus

n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_husky
n02096294 4 Australian_terrier
n02102040 5 English_springer
n02066245 6 grey_whale
n02509815 7 lesser_panda
n02124075 8 Egyptian_cat
n02417914 9 ibex
n02123394 10 Persian_cat

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

myusuf3

To remove a submodule you need to:

• Delete the relevant section from the .gitmodules file.
• Stage the .gitmodules changes git add .gitmodules
• Delete the relevant section from .git/config.
• Run git rm --cached path_to_submodule (no trailing slash).
• Run rm -rf .git/modules/path_to_submodule (no trailing slash).
• Commit git commit -m "Removed submodule "
• Delete the now untracked submodule files rm -rf path_to_submodule