grantslatton

import urllib2
import re
import sys
from collections import defaultdict
from random import random

"""
PLEASE DO NOT RUN THIS QUOTED CODE FOR THE SAKE OF daemonology's SERVER, IT IS 
NOT MY SERVER AND I FEEL BAD FOR ABUSING IT. JUST GET THE RESULTS OF THE 
CRAWL HERE: http://pastebin.com/raw.php?i=nqpsnTtW AND SAVE THEM TO "archive.txt"

kevin-smets

Default

Powerlevel10k

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






\

woollsta

/**
 * Chunkify
 * Google Chrome Speech Synthesis Chunking Pattern
 * Fixes inconsistencies with speaking long texts in speechUtterance objects 
 * Licensed under the MIT License
 *
 * Peter Woolley and Brett Zamir
 */
 
var speechUtteranceChunker = function (utt, settings, callback) {

kalinchernev

Afghanistan
Albania
Algeria
Andorra
Angola
Antigua & Deps
Argentina
Armenia
Australia
Austria

nylki

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

kukuruza

from math import sqrt

def put_kernels_on_grid (kernel, pad = 1):

  '''Visualize conv. filters as an image (mostly for the 1st layer).
  Arranges filters into a grid, with some paddings between adjacent filters.

  Args:
    kernel:            tensor of shape [Y, X, NumChannels, NumKernels]
    pad:               number of black pixels around each filter (between them)

danijar

# Example for my blog post at:
# https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
import functools
import sets
import tensorflow as tf


def lazy_property(function):
    attribute = '_' + function.__name__

tanemaki

basic_rl (v.0.0.3)

A basic_rl.py provides a simple implementation of SARSA/Q-learning algorithms (specified by -a flag) with epsilon-greedy/softmax policies (specified by -p flag). You can also select the environment other than Roulette-v0 using -e flag. It also generates a graphical summary of your simulation.

Type the following command in your console to run the simulation using the default setting.

chmod +x basic_rl.py
./basic_rl.py

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