I hereby claim:
- I am rbrigden on github.
- I am rbrigden (https://keybase.io/rbrigden) on keybase.
- I have a public key ASC8BblZeXDpMM3sqyN4ikMdSgUKf-iwFBtjUVJgdRJ4Bwo
To claim this, I am signing this object:
#!/usr/bin/python | |
# Welcome to the classic game of Snake! Follow the instructions in the lesson | |
# to complete the code and play the game. Have fun! | |
# author: Ryan Brigden | |
# date: 6/11/15 | |
# import modules | |
import pygame, random, sys |
class Recorder: | |
def __init__(self): | |
self.log = logging.getLogger('capturadio.recorder') | |
self.start_time = None | |
def capture(self, show): | |
config = Configuration() | |
self.log.info(u'capture "%s" from "%s" for %s seconds to %s' %\ | |
(show.name, show.station.name, show.duration, config.destination)) |
# | |
# VPython shell program to display and plot the Earth's motion about the Sun, | |
# including its angular momentum vector and its Runge-Lenz vector. | |
# | |
from visual import * | |
from visual.graph import * | |
# | |
# Define the display window. The range sets the scale for all arrows. | |
# | |
display(title = 'Planetary Orbit', width = 600, height = 600, range = 3e11) |
require 'mechanize' | |
require "httparty" | |
class DirectoryAPI | |
def initialize | |
@token = 'nJ+gXjL+ZpbdhuNR700AT7H3AEQ5r2/zB1+8DSWAl/Y=' | |
@base_url = 'https://directory.andrew.cmu.edu/' | |
@mechanize = Mechanize.new |
I hereby claim:
To claim this, I am signing this object:
#!/bin/python | |
# author: Ryan Brigden | |
# The "brute" in brute force | |
# Question: Given a (large) list of words and a list of top-level domains (TLDs) | |
# from the Internet Assigned Numbers Authority (IANA), such as ".com" and ".net", | |
# find all of the possible "singleton" domains that can be registered with words | |
# from the word list. A singleton domain is defined as a sensical word (ie from | |
# the word list) whose suffix is a legitimate TLD (ie from the TLD list). You are | |
# given a function (is_available) that checks whether a given domain name is |
The original assignment set forth instructed students to implement a feedforward artificial neural network (ANN) in a relatively low level language or framework. Although higher level scripting languages such as Python and Lua have wrapped heavily optimized libraries that perform the same functions, the goal of this assignment is to truly understand the theoretical underpinnings of feedforward neural networks by writing the routines from scratch (almost).
abdominocardi.ac | |
autotr.actor | |
cephalotr.actor | |
cocontr.actor | |
coen.actor | |
cornf.actor | |
counter.actor | |
effr.actor | |
idemf.actor | |
lithofr.actor |
abdominocardiac | abdominocardi.ac | |
---|---|---|
autotractor | autotr.actor | |
cephalotractor | cephalotr.actor | |
cocontractor | cocontr.actor | |
coenactor | coen.actor | |
cornfactor | cornf.actor | |
counteractor | counter.actor | |
effractor | effr.actor | |
idemfactor | idemf.actor | |
lithofractor | lithofr.actor |
from __future__ import division, print_function, absolute_import | |
import tensorflow as tf | |
import numpy as np | |
import matplotlib.pyplot as plt | |
# Import MNIST data | |
from tensorflow.examples.tutorials.mnist import input_data | |
mnist = input_data.read_data_sets("MNIST_data", one_hot=False) |