machinaut

FROM ubuntu:18.04

# Install python and utils
RUN apt-get update && apt-get install -y python3-pip curl unzip \
    libosmesa-dev libglew-dev patchelf libglfw3-dev

# Download mujoco
RUN curl https://www.roboti.us/download/mjpro150_linux.zip --output /tmp/mujoco.zip && \
    mkdir -p /root/.mujoco && \
    unzip /tmp/mujoco.zip -d /root/.mujoco && \

thomelane

import gym
from IPython import display
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

env = gym.make('Breakout-v0')
env.reset()
img = plt.imshow(env.render(mode='rgb_array')) # only call this once
for _ in range(100):

anishathalye

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would

kastnerkyle

# Based on code from Jose Sotelo
# Author: Kyle Kastner
# License: BSD 3-Clause
from __future__ import print_function
import os
import shutil
import subprocess
import stat

vctkdir = "/Tmp/kastner/vctk/VCTK-Corpus/"

agnanachandran

import math
import random

def get_random_neighbour(state):
    neighbour = [house[:] for house in state] # Deep copy

    i, j = random.sample(xrange(5), 2)
    attr_idx = random.randint(0, 4)

    neighbour[i][attr_idx], neighbour[j][attr_idx] = neighbour[j][attr_idx], neighbour[i][attr_idx]

anj1

import types
import tensorflow as tf 
import numpy as np

# Expressions are represented as lists of lists,
# in lisp style -- the symbol name is the head (first element)
# of the list, and the arguments follow.

# add an expression to an expression list, recursively if necessary.
def add_expr_to_list(exprlist, expr):

mikelove

n <- 200
m <- 40
set.seed(1)
x <- runif(n, -1, 1)
library(rafalib)
bigpar(2,2,mar=c(3,3,3,1))
library(RColorBrewer)
cols <- brewer.pal(11, "Spectral")[as.integer(cut(x, 11))]
plot(x, rep(0,n), ylim=c(-1,1), yaxt="n", xlab="", ylab="", 
     col=cols, pch=20, main="underlying data")

mickypaganini

samim23

# "Colorizing B/W Movies with Neural Nets", 
# Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
# BACKGROUND: http://tinyclouds.org/colorize/
# DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4
# USAGE:
# 1. Download TensorFlow model from: http://tinyclouds.org/colorize/
# 2. Use FFMPEG or such to extract frames from video.
# 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands:
# mogrify -resize 224x224 *.jpg
# mogrify -gravity center -background black -extent 224x224 *.jpg