cryptid11

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

Python 2.x

$ python -m SimpleHTTPServer 8000

Can you imagine a computer can generate an nba article?

The following is some nba articles fully-automatically generated by char-cnn, a recurrent-neural-network library thanks to Andrej Karpathy [link]. The library is awesome to easy, and very user-friendly. You should try it! :)

Basically, I wrote a python script [link] to extract past archives . And use that as the training set for the recurrent neural network.

The articles below are generated by a network trained with rougly about 2 millions character (which is an okay size; not big enough though). You can see that the generated article contains artificial author names, speeches, etc; similar to an nba archive (although the logic has to be improved, it is FUN.)

You can tune the parameter and train with a even bigger dataset using my script. And you will probably get better result! Have fun:)

	#!/usr/bin/python

	import socket
	import struct
	import sys

	# We want unbuffered stdout so we can provide live feedback for
	# each TTL. You could also use the "-u" flag to Python.
	class flushfile(file):
	def __init__(self, f):

	#!/bin/bash -e
	clear
	echo "============================================"
	echo "WordPress Install Script"
	echo "============================================"
	echo "Database Name: "
	read -e dbname
	echo "Database User: "
	read -e dbuser
	echo "Database Password: "

	import base64
	import json
	import matplotlib, matplotlib.pyplot
	import numpy
	import types

	def show_plot(width, height=None):
	"""
	A decorator -- show the matplotlib plot after `f` completes.
	Takes optional parameters (width, height) determining the size of the plot.

	"""
	MIT License

	Copyright (c) 2017 Cyrille Rossant

	Permission is hereby granted, free of charge, to any person obtaining a copy
	of this software and associated documentation files (the "Software"), to deal
	in the Software without restriction, including without limitation the rights
	to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	copies of the Software, and to permit persons to whom the Software is

	# The MIT License (MIT)
	# Copyright (c) 2016 Vladimir Ignatev
	#
	# Permission is hereby granted, free of charge, to any person obtaining
	# a copy of this software and associated documentation files (the "Software"),
	# to deal in the Software without restriction, including without limitation
	# the rights to use, copy, modify, merge, publish, distribute, sublicense,
	# and/or sell copies of the Software, and to permit persons to whom the Software
	# is furnished to do so, subject to the following conditions:
	#

	OP_DEPTH 3 OP_EQUAL
	OP_IF
	OP_HASH160 <hash160(R)> OP_EQUALVERIFY
	OP_0 2 <AlicePubkey1> <BobPubkey1> 2 OP_CHECKMULTISIG
	OP_ELSE
	OP_0 2 <AlicePubkey2> <BobPubkey2> 2 OP_CHECKMULTISIG
	OP_END

	"""
	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)

	ffmpeg -i input_file.mp4 -vf scale=320:-1:flags=lanczos,fps=30 frames/ffout%03d.png
	convert -loop 0 frames/ffout*.png output_file.gif