dbr

"""Playing with tornado.websocket, to add markers to a Google Map using WebSockets

$ pip install tornado
$ python livemap.py --port=8888

Open http://localhost:8888 in one window

Each time http://localhost:8888/ping is opened in a second window, a
new marker is added to the map (at a random location)

zupo

# -*- coding: utf-8 -*-
# @author: Peter Lamut

import argparse
import os
import shutil

N = 10  # the number of files in seach subfolder folder

samklr

##### Install a lot of stuff first #####

$sudo apt-get update

##install python 
$ wget http://09c8d0b2229f813c1b93-c95ac804525aac4b6dba79b00b39d1d3.r79.cf1.rackcdn.com/Anaconda-2.0.1-Linux-x86_64.sh
$ sudo bash anaconda........sh
 
##install necessary libs
$ sudo apt-get install -y python-matplotlib python-tornado ipython ipython-notebook python-setuptools python-pip

patriciogonzalezvivo

This tutorial is partialy based on Yuriy’s Czoli article ‘Processing LiDAR to extract building heights’.

Install PostGIS

On Mac OSX

• Install Postgres, PostGIS and OSM-PostGIS tools

brew install postgis 

jqtrde

Libraries for a modern geospatial workflow

Distribution

• Writing, Running, and Distributing
• Tox driven development
• Publishing python libraries

Numpy

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

bayesian_neural_network.ipynb

yaroslavvb

#!/usr/bin/env python
# Benchmark transferring data, part of troubleshooting https://github.com/tensorflow/tensorflow/issues/6116
#
# Take a independent workers communicating with b parameter shards
# Each worker tries to add to variables stored on parameter server as fast as
# possible.
# 
# macbook
# ps=1: 1.6 GB/s
# ps=2: 2.6 GB/s

peterroelants

"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim
from tensorflow.contrib.learn import ModeKeys
from tensorflow.contrib.learn import learn_runner


# Show debugging output