(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.
(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.
Locate the section for your github remote in the .git/config
file. It looks like this:
[remote "origin"]
fetch = +refs/heads/*:refs/remotes/origin/*
url = git@github.com:joyent/node.git
Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/*
to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:
Using Python's built-in defaultdict we can easily define a tree data structure:
def tree(): return defaultdict(tree)
That's it!
The count of contributions (summary of Pull Requests, opened issues and commits) to public repos at GitHub.com from Wed, 21 Sep 2022 till Thu, 21 Sep 2023.
Only first 1000 GitHub users according to the count of followers are taken. This is because of limitations of GitHub search. Sorting algo in pseudocode:
githubUsers
.filter(user => user.followers > 1000)
### MATPLOTLIBRC FORMAT | |
# This is a sample matplotlib configuration file - you can find a copy | |
# of it on your system in | |
# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it | |
# there, please note that it will be overridden in your next install. | |
# If you want to keep a permanent local copy that will not be | |
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux | |
# like systems) and C:\Documents and Settings\yourname\.matplotlib | |
# (win32 systems). |
#!/usr/bin/env sh | |
## | |
# This is script with usefull tips taken from: | |
# https://github.com/mathiasbynens/dotfiles/blob/master/.osx | |
# | |
# install it: | |
# curl -sL https://raw.github.com/gist/2108403/hack.sh | sh | |
# |
* { | |
font-size: 12pt; | |
font-family: monospace; | |
font-weight: normal; | |
font-style: normal; | |
text-decoration: none; | |
color: black; | |
cursor: default; | |
} |
"""Kernel K-means""" | |
# Author: Mathieu Blondel <mathieu@mblondel.org> | |
# License: BSD 3 clause | |
import numpy as np | |
from sklearn.base import BaseEstimator, ClusterMixin | |
from sklearn.metrics.pairwise import pairwise_kernels | |
from sklearn.utils import check_random_state |
Ubuntu 15.10 have been released for a couple of days. It is a bleeding-edge system coming with Linux kernel 4.2 and GCC 5. However, compiling and running Caffe on this new system is no longer as smooth as on earlier versions. I have done some research related to this issue and finally find a way out. I summarize it here in this short tutorial and I hope more people and enjoy this new system without breaking their works.
The latest NVIDIA driver is officially included in Ubuntu 15.10 repositories. One can install it directly via apt-get
.
sudo apt-get install nvidia-352-updates nvidia-modprobe
The nvidia-modprobe
utility is used to load NVIDIA kernel modules and create NVIDIA character device files automatically everytime your machine boots up.
Reboot your machine and verify everything works by issuing nvidia-smi
or running deviceQuery
in CUDA samples.
The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:
PyTorch defines a new package torch
. In this post we will consider the ._C
module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor
) and to call C/C++ functions.