mrgloom

"""
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

mrgloom

What

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

mrgloom

+/*
+ * Author: Samyak Datta (datta[dot]samyak[at]gmail.com)
+ *
+ * A program to demonstrate contour detection using the Active Contour
+ * Model (Snake). The cvSnakeImage() method is applied to detect lip
+ * contour in a lip ROI image.
+ *
+ */
+
+#include "opencv2/highgui/highgui.hpp"

mrgloom

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:

mrgloom

#include "CsvWriter.h"

#include <windows.h>

#include <time.h> // for random number generator

#include <string>
#include <vector>
#include <fstream> // std::ofstream

mrgloom

%gpuDevice() %info about GPU
clear
gpuDevice(1); %reset GPU device

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%test matrix multiplication
try
%n= 100;
for m=100:100:10000
    n=m;

mrgloom

>> a = [1 2 3; 2 5 7; 3 7 9]

a =

     1     2     3
     2     5     7
     3     7     9

>> [U S V] = svd(a)

mrgloom

# 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

mrgloom

# Mathieu Blondel, September 2010
# License: BSD 3 clause

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)

mrgloom

import numpy as np
import time

def read_data():
	#M x N
	data= np.loadtxt("data_3d.txt",delimiter=" ", skiprows=1, usecols=(0,1,2))
	print data.shape
	# print data
	return data