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!






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jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname

bwhite

"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

henrik

tmux cheatsheet

As configured in my dotfiles.

start new:

tmux

start new with session name:

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

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

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

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

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