arthurzhukovski

:: The following line is neccessary if you need an ability to restart the streams with this batch file
:: Kill all existing streams (the command actually suspends ALL the vlc processes):
taskkill /f /im "vlc.exe"

:: Run two instances of VLC. These would transcode MP4 rtsp-stream to Motion JPEG http-stream:
start vlc -vvv -Idummy rtsp://login:password@192.168.0.2/streaming/channels/2/preview --sout #transcode{vcodec=MJPG,venc=ffmpeg{strict=1},fps=10,width=640,height=360}:standard{access=http{mime=multipart/x-mixed-replace;boundary=--7b3cc56e5f51db803f790dad720ed50a},mux=mpjpeg,dst=:9911/}
start vlc -vvv -Idummy rtsp://login:password@192.168.0.3/streaming/channels/2/preview --sout #transcode{vcodec=MJPG,venc=ffmpeg{strict=1},fps=10,width=640,height=360}:standard{access=http{mime=multipart/x-mixed-replace;boundary=--7b3cc56e5f51db803f790dad720ed50a},mux=mpjpeg,dst=:9912/}

:: In order to execute VLC with `vlc` as in exapmle above, you have to add corresponding value to the PATH variable.
:: Otherwise you have t

bigsnarfdude

General Nanodegree Information
Nanodegrees Programs: https://www.udacity.com/nanodegree
Nanodegree Plus (job guarantee): https://www.udacity.com/nanodegree/plus 
UConnect (weekly in-person study sessions):  https://www.udacity.com/uconnect 

Courses on Udacity
Machine Learning Engineer Nanodegree by Google (Currently Available): https://www.udacity.com/course/machine-learning-engineer-nanodegree-by-google--nd009
Artificial Intelligence for Robots (Free Course) https://www.udacity.com/course/artificial-intelligence-for-robotics--cs373
Intro to Statistics (Free Course) https://www.udacity.com/course/intro-to-statistics--st101
Deep Learning (Free Course) https://www.udacity.com/course/deep-learning--ud730

benjbaron

#include <QtGui>
#include <QGraphicsRectItem>
#include <QGraphicsView>
#include <QApplication>
#include <QGraphicsSceneMouseEvent>

class CustomItem : public QGraphicsEllipseItem
{
protected:
    void mousePressEvent(QGraphicsSceneMouseEvent *event)

karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

sztupy

rage-quit plugin for oh-my-zsh

based on rage-quit support for bash

HOW TO INSTALL

Put the files below inside ~/.oh-my-zsh/custom/plugins/fuck

Also chmod a+x the flip command.

jamesejr

Integer      | Array   |
Directive    | Element | Meaning
---------------------------------------------------------------------------
   C         | Integer | 8-bit unsigned (unsigned char)
   S         | Integer | 16-bit unsigned, native endian (uint16_t)
   L         | Integer | 32-bit unsigned, native endian (uint32_t)
   Q         | Integer | 64-bit unsigned, native endian (uint64_t)
             |         |
   c         | Integer | 8-bit signed (signed char)
   s         | Integer | 16-bit signed, native endian (int16_t)

nicolashery

Environment variables in Jekyll templates

This is one way to pass some data (API tokens, etc.) to your Jekyll templates without putting it in your _config.yml file (which is likely to be committed in your GitHub repository).

Copy the environment_variables.rb plugin to your _plugins folder, and add any environment variable you wish to have available on the site.config object.

In a Liquid template, that information will be available through the site object. For example, _layouts/default.html could contain:

gist:4374633

1.upto(100){|n|puts'FizzBuzz
'[o=n**4%-15,o+13]||n}

jarosan

# Run with: rake environment elasticsearch:reindex

namespace :elasticsearch do
  desc "re-index elasticsearch"
  task :reindex => :environment do

    klass = Place
    ENV['CLASS'] = klass.name
    ENV['INDEX'] = new_index = klass.tire.index.name << '_' << Time.now.strftime('%Y%m%d%H%M%S')

amueller

import numpy as np
import warnings

from itertools import cycle, izip

from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelBinarizer