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@pazdera
pazdera / builder.cpp
Created August 2, 2011 20:33
Example of `builder' design pattern in C++
/*
* Example of `builder' design pattern.
* Copyright (C) 2011 Radek Pazdera
*
* This program is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
@kazad
kazad / fourier.html
Created June 25, 2014 19:00
BetterExplained Fourier Example
<html>
<head>
<script src="//cdnjs.cloudflare.com/ajax/libs/underscore.js/1.4.2/underscore-min.js"></script>
<script src="//ajax.googleapis.com/ajax/libs/jquery/1.8.2/jquery.min.js"></script>
<script src="//cdnjs.cloudflare.com/ajax/libs/modernizr/2.6.2/modernizr.min.js"></script>
<script src="//ajax.cdnjs.com/ajax/libs/json2/20110223/json2.js"></script>
<!--
TODO:
@shagunsodhani
shagunsodhani / Batch Normalization.md
Last active July 25, 2023 18:07
Notes for "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" paper

The Batch Normalization paper describes a method to address the various issues related to training of Deep Neural Networks. It makes normalization a part of the architecture itself and reports significant improvements in terms of the number of iterations required to train the network.

Issues With Training Deep Neural Networks

Internal Covariate shift

Covariate shift refers to the change in the input distribution to a learning system. In the case of deep networks, the input to each layer is affected by parameters in all the input layers. So even small changes to the network get amplified down the network. This leads to change in the input distribution to internal layers of the deep network and is known as internal covariate shift.

It is well established that networks converge faster if the inputs have been whitened (ie zero mean, unit variances) and are uncorrelated and internal covariate shift leads to just the opposite.

@mhweber
mhweber / explode.py
Created July 25, 2016 17:45 — forked from debboutr/explode.py
Explode MultiPolygon geometry into individual Polygon geometries in a shapefile using GeoPandas and Shapely
import geopands as gpd
from shapely.geometry.polygon import Polygon
from shapely.geometry.multipolygon import MultiPolygon
def explode(indata):
indf = gpd.GeoDataFrame.from_file(indata)
outdf = gpd.GeoDataFrame(columns=indf.columns)
for idx, row in indf.iterrows():
if type(row.geometry) == Polygon:
outdf = outdf.append(row,ignore_index=True)
@piiswrong
piiswrong / install.sh
Last active December 12, 2017 07:58
install mxnet on ubuntu
#!/usr/bin/env bash
set -e
# install cuda-7.5
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1404/x86_64/cuda-repo-ubuntu1404_7.5-18_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1404_7.5-18_amd64.deb
sudo apt-get update
sudo apt-get install -y linux-image-extra-`uname -r` linux-headers-`uname -r` linux-image-`uname -r`
sudo apt-get install -y cuda-7-5
echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:\$LD_LIBRARY_PATH" | tee -a ~/.profile | tee -a ~/.bashrc
@brannondorsey
brannondorsey / pix2pix_paper_notes.md
Last active January 3, 2022 09:57
Notes on the Pix2Pix (pixel-level image-to-image translation) Arxiv paper

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
@haskaalo
haskaalo / tarcheatsheet.md
Last active July 12, 2024 06:35
Tar usage / Tar Cheat Sheet

Tar Usage / Cheat Sheet

Compress a file or directory

e.g: tar -czvf name-of-archive.tar.gz /path/to/directory-or-file

  • -c: Create an archive.
  • -z: Compress the archive with gzip.
  • -v: makes tar talk a lot. Verbose output shows you all the files being archived and much.
  • -f: Allows you to specify the filename of the archive.
@lirnli
lirnli / Gaussian Mixture Model.ipynb
Created September 18, 2017 01:45
Gaussian Mixture Model
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@alsrgv
alsrgv / horovod_model_parallelism.py
Created January 27, 2018 06:20
Model parallelism in Horovod
# Copyright 2018 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
@dalgu90
dalgu90 / install_tree_local.sh
Created February 19, 2018 03:03
Install linux tree command on local path
#!/bin/sh
PREFIX="$HOME/.local/"
install_tree() {
# The project page of linux "tree" command is located at http://mama.indstate.edu/users/ice/tree
TMP_TREE_DIR="/tmp/$USER/tree"; mkdir -p $TMP_TREE_DIR
wget -nc -O $TMP_TREE_DIR/tree.tgz "http://mama.indstate.edu/users/ice/tree/src/tree-1.7.0.tgz"
tar -xvzf $TMP_TREE_DIR/tree.tgz -C $TMP_TREE_DIR --strip-components 1