aconz2

// ==UserScript==
// @name         Upwork AutoRedirect
// @namespace    http://tampermonkey.net/
// @version      0.1
// @description  Automatically redirect you to the external page when clicking on an external link from an Upwork listing.
// @author       aconz2
// @match        https://www.upwork.com/leaving?*
// @grant        none
// ==/UserScript==

aconz2

aconz2

using CUDA on GPU 0...	
Make sure that your saved checkpoint was also trained with GPU. If it was trained with CPU use -gpuid -1 for sampling as well	
creating an lstm...	
missing seed text, using uniform probability over first character	
--------------------------	
�y job is producing how people read out there. Our platform builds a core technology startup is established by hardest lifestal search and data structures and allowing us to be family on all shapes and shape some of the developers, including MacBook profiles, mongotiout and test achieved as the time to distribute the code. If you have any questions about all of our jobs page for more information to about us really among the latter to contribute to person because it's a very larger hands to do the intersection that lesss more varied or email me (http://blog.vittualsnownup.org/wiki/) – Full-Time, , Canada, Remote, Perl, POS, (or JavaScript (UI/UX)TEDY is a number of hiring software developers who are excited about technical hires to be quickly as th

aconz2

type exp =
  | Int of int
  | Succ of exp
  | Plus of exp * exp

(* realistically we would want to specify depth and/or the bias of
choosing a certain expression *)
let rec gen_exp () : exp =
  match Random.int 3 with
  | 0 -> Int (Random.int 42)

aconz2

type exp =
  | Int of int
  | Succ of exp

(* This is what we want to write: *)
(* let rec step (e : exp) : exp option = *)
(*   match e with *)
(*   | Int _ -> None *)
(*   | Succ (Int i) -> Some (Int (i + 1)) *)
(*   | Succ e -> Some (Succ (step e)) *)

aconz2

#!/usr/bin/env python3

from matplotlib import pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D

normed = lambda v: v / np.linalg.norm(v)
# orthogonal vector in 2 space
orthogonal = lambda v: np.cross(v, [0, 0, 1])[:2]
pltvec = lambda v, **kwargs: plt.plot([0, v[0]], [0, v[1]], **kwargs)

aconz2

from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# original data
# not linearly separable in 2D
x = np.array([[1, 1], [-1, -1], [-1, 1], [1, -1]])

aconz2

import numpy as np
from matplotlib import pyplot as plt
plt.ion()

# say you have a bunch of distributions you'd like to look at
data_sets = [np.random.poisson(lam, size=100), for lam in np.random.randint(1, 100, size=10)]

# in a REPL, this one-liner makes it easy to plot each one successively waiting for ENTER from the terminal
_ = [[plt.hist(data), input()] for data in data_sets]

aconz2

#!/usr/bin/env python3

import pip

searcher = pip.commands.SearchCommand()
options, _ = searcher.parse_args([])
hits = searcher.search('*', options)
hits_transformed = pip.commands.search.transform_hits(hits)
for hit in hits_transformed:
  print(hit['name'])

aconz2

/**
 * Expose `Emitter`.
 */

module.exports = Emitter;

/**
 * Initialize a new `Emitter`.
 *
 * @api public