| from __future__ import absolute_import, division, print_function | |
| import multiprocessing | |
| import pickle | |
| from multiprocessing.pool import ThreadPool | |
| from celery import shared_task | |
| from dask.local import get_async # TODO: get better get | |
| from dask.context import _globals | |
| from dask.optimize import fuse, cull |
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
| """ | |
| NMF by coordinate descent, designed for sparse data (without missing values) | |
| """ | |
| # Author: Mathieu Blondel <mathieu@mblondel.org> | |
| # License: BSD 3 clause | |
| import numpy as np | |
| import scipy.sparse as sp | |
| import numba |
| """ | |
| A deep neural network with or w/o dropout in one file. | |
| License: Do What The Fuck You Want to Public License http://www.wtfpl.net/ | |
| """ | |
| import numpy, theano, sys, math | |
| from theano import tensor as T | |
| from theano import shared | |
| from theano.tensor.shared_randomstreams import RandomStreams |
(by @andrestaltz)
If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.
A simple example of two-way communication between IPython notebook and javascript, using a D3 widget.
View this notebook on nbviewer, but be aware that it won't work without being connected to an IPython kernel!
It's better to download the notebook itself and open it with IPython notebook.
Find it here: https://github.com/bitemyapp/learnhaskell
| import os | |
| def main(): | |
| os.chdir(os.path.dirname(os.path.dirname(__file__))) | |
| src_path = "resources" | |
| dest_path = "myproject/res" | |
| for filename in os.listdir(src_path): | |
| if not os.path.isfile(os.path.join(src_path, filename)): |
For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.
A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common s
