upsuper

#include <iostream>
using namespace std;

template<typename T>
class Complex {
    private:
        T m_real;
        T m_imag;
    public:
        Complex<T>(T real=0.0, T imag=0.0)

aroemen

#!/bin/sh

set -e

git config -f .gitmodules --get-regexp '^submodule\..*\.path$' |
    while read path_key path
    do
        url_key=$(echo $path_key | sed 's/\.path/.url/')
        url=$(git config -f .gitmodules --get "$url_key")
        git submodule add $url $path

evanscottgray

docker ps | awk {' print $1 '} | tail -n+2 > tmp.txt; for line in $(cat tmp.txt); do docker kill $line; done; rm tmp.txt

Chaser324

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

uupaa

Image source

https://gyazo.com/eb5c5741b6a9a16c692170a41a49c858.png

Try resize it!

• ![](https://gyazo.com/eb5c5741b6a9a16c692170a41a49c858.png | width=100)

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

ChunMinChang

#!/usr/bin/python
import re
import sys

def removeComments(text):
    """ remove c-style comments.
        text: blob of text with comments (can include newlines)
        returns: text with comments removed
    """
    pattern = r"""

zoltanctoth

from pyspark.sql.types import StringType
from pyspark.sql.functions import udf

maturity_udf = udf(lambda age: "adult" if age >=18 else "child", StringType())

df = spark.createDataFrame([{'name': 'Alice', 'age': 1}])
df.withColumn("maturity", maturity_udf(df.age))

df.show()

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

protrolium

Install ImageMagick

brew install ImageMagick

Pull specific region of frames from video file w/ ffmpeg

ffmpeg -ss 14:55 -i video.mkv -t 5 -s 480x270 -f image2 %04d.png

• -ss 14:55 gives the timestamp where I want FFmpeg to start, as a duration string.
  
• -t 5 says how much I want FFmpeg to decode, using the same duration syntax as for -ss.
  
• -s 480x270 tells FFmpeg to resize the video output to 480 by 270 pixels.
  
• -f image2 selects the output format, a series of still images — make sure there are leading zeros in filename.