Cyberek

So you were able to install Kodi via "sudo apt-get install kodi" but have no idea how to force it to autostart on boot?

You have tried all those googled solutions such as adding kodi-standalone to .bashrc, creating init.d script but nothing worked?

This is the right place to get the answer.

For some reason, the current version of Kodi doesnt provide 2 important files:
/etc/init.d/kodi
/etc/default/kodi

georgexsh

import sys

def j(lineno):
    frame = sys._getframe().f_back
    called_from = frame

    def hook(frame, event, arg):
        if event == 'line' and frame == called_from:
            try:
                frame.f_lineno = lineno

llllllllll

from itertools import chain, repeat
import sys
import types

# byte offsets for structures
if hasattr(sys, 'gettotalrefcount'):
    # under PyDEBUG builds
    _f_localsplus_offset = 392
    _ob_item_offset = 40
else:

artsobolev

Note: I'm updating this gist as I encounter new reviews, so make sure you're reading the latest revision!

Just as the previous year I collected (and keep doing so) links to various summaries and takeaways from this year's NIPS.

• NIPS 2016 Symposium on People and machines: Public views on machine learning, and what this means for machine learning researchers. (Notes and panel discussion) by /u/gcr
• NIPS 2016 summary, wrap up, and links to slides by /u/beamsearch
• Post NIPS Reflections by Neil Lawrence
• [Some general take aways from #NIPS2016](https://medium.com/@IgorCarron/some-general-take-aways-from-ni

brannondorsey

Image-to-Image Translation with Conditional Adversarial Networks

Notes from arXiv:1611.07004v1 [cs.CV] 21 Nov 2016

• Euclidean distance between predicted and ground truth pixels is not a good method of judging similarity because it yields blurry images.
• GANs learn a loss function rather than using an existing one.
• GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss.
• Conditional GANs (cGANs) learn a mapping from observed image x and random noise vector z to y: y = f(x, z)
• The generator G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".
• The discriminator D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.
• Unlike an unconditional GAN, both th

gauravbarthwal

Download/Copy all related *.zip files in one directory.
Open terminal and change to that directory which has all zip files.
Enter command zip -s- FILE_NAME.zip -O COMBINED_FILE.zip
Enter unzip COMBINED_FILE.zip

whophil

# After Ubuntu 16.04, Systemd becomes the default. 
# It is simpler than https://gist.github.com/Doowon/38910829898a6624ce4ed554f082c4dd

[Unit]
Description=Jupyter Notebook

[Service]
Type=simple
PIDFile=/run/jupyter.pid
ExecStart=/home/phil/Enthought/Canopy_64bit/User/bin/jupyter-notebook --config=/home/phil/.jupyter/jupyter_notebook_config.py

karenyyng

Table Of Content

Skip to the relevant sections if needed.

• 2-min tutorial to do it the quick-and-dirty-way
• Concepts for resolving Git conflicts
• Setting up different editors / tool for using git mergetool
  • Finding out what mergetool editors are supported
• mergetool simple code example for vimdiff
  • Resolving conflict from a Git pull
• Other great references and tutorials

tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






\

philipbjorge

"""
Python's set data structure is the only one without a literal
notation for an empty set.

http://excess.org/article/2012/11/python-container-literals/

This is an ast transformer to add an empty set notation with the
following form:

    empty_set = {_}