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mrgloom / Convolutional Arithmetic.ipynb
Created May 12, 2017 — forked from akiross/Convolutional Arithmetic.ipynb
Few experiments on how convolution and transposed convolution (deconvolution) should work in tensorflow.
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mrgloom /
Created Mar 15, 2017 — forked from kastnerkyle/
Online statistics in numpy
# Author: Kyle Kaster
# License: BSD 3-clause
import numpy as np
def online_stats(X):
Converted from John D. Cook
mrgloom / detect_multiscale.cpp
Created May 23, 2016 — forked from thorikawa/detect_multiscale.cpp
Simple example for CascadeClassifier.detectMultiScale
View detect_multiscale.cpp
#include <opencv2/opencv.hpp>
#include <vector>
using namespace cv;
using namespace std;
int main () {
Mat img = imread("lena.jpg");
CascadeClassifier cascade;
if (cascade.load("haarcascade_frontalface_alt.xml")) {
class OnlineLearner(object):
def __init__(self, **kwargs):
self.last_misses = 0.
self.iratio = 0. = 1.
self.l = kwargs["l"]
self.max_ratio = -np.inf
self.threshold = 500.
def hinge_loss(self, vector, cls, weight):
mrgloom /
Last active Sep 11, 2015
Some fairly clean (and fast) code for Restricted Boltzmann machines.
Code for training RBMs with contrastive divergence. Tries to be as
quick and memory-efficient as possible while utilizing only pure Python
and NumPy.
# Copyright (c) 2009, David Warde-Farley
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
mrgloom /
Last active Aug 31, 2015 — forked from larsmans/
k-means feature mapper for scikit-learn
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.metrics.pairwise import rbf_kernel
class KMeansTransformer(BaseEstimator, TransformerMixin):
def __init__(self, centroids):
self.centroids = centroids
def fit(self, X, y=None):
return self
mrgloom /
Last active Feb 9, 2018 — forked from syhw/
comparing SGD vs SAG vs Adadelta vs Adagrad
A deep neural network with or w/o dropout in one file.
import numpy
import theano
import sys
import math
from theano import tensor as T
from theano import shared


Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

  • An active AWS account. First time sign-ups are eligible for the free tier for a year
  • One Micro Tier EC2 Instance
  • With AWS we will use the stock Ubuntu Server AMI and customize it.
  • Anaconda for Python.
  • Coffee/Beer/Time


I've been interested in computer vision for a long time, but I haven't had any free time to make any progress until this holiday season. Over Christmas and the New Years I experimented with various methodologies in OpenCV to detect road signs and other objects of interest to OpenStreetMap. After some failed experiments with thresholding and feature detection, the excellent /r/computervision suggested using the dlib C++ module because it has more consistently-good documentation and the pre-built tools are faster.

After a day or two figuring out how to compile the examples, I finally made some progress:

Compiling dlib C++ on a Mac with Homebrew

  1. Clone dlib from Github to your local machine:
# clear the workspace
rm(list = ls())
# load the relevant libraries
# install.packages(rCUR)
library(rCUR) # for CUR decomposition
# install.packages(irlba)
library(irlba) # for fast svd