arasharchor

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

arasharchor

arasharchor

Deep Learning for Face Recognition (May 2016)

Popular architectures

• FaceNet (Google)
  • They use a triplet loss with the goal of keeping the L2 intra-class distances low and inter-class distances high
• DeepID (Hong Kong University)
  • They use verification and identification signals to train the network. Afer each convolutional layer there is an identity layer connected to the supervisory signals in order to train each layer closely (on top of normal backprop)
• DeepFace (Facebook)
  • Convs followed by locally connected, followed by fully connected

arasharchor

import matplotlib.pyplot as plt

def draw_neural_net(ax, left, right, bottom, top, layer_sizes):
    '''
    Draw a neural network cartoon using matplotilb.
    
    :usage:
        >>> fig = plt.figure(figsize=(12, 12))
        >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])
    

arasharchor

#include <opencv2/core/core.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <iostream>
#include <fcntl.h>
#include <unistd.h>
#include <termios.h>

using namespace std;
using namespace cv;

arasharchor

#include <Servo.h>

#define MIN_Y 90
#define MAX_Y 180

#define MIN_X 0
#define MAX_X 180

Servo tilt;
Servo pan;

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MapRoulette Challenge Tutorial (ßeta)

You have played MapRoulette. You have seen some of the fun challenges. If you are reading this, you are probably thinking: 'I have a great idea for the next MapRoulette challenge!'

Great! That is exactly what I am here to explain step by step. So let's get started!

Step 1 - The Idea

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'''
This is how to track a white ball example using SimpleCV

The parameters may need to be adjusted to match the RGB color
of your object.

The demo video can be found at:
http://www.youtube.com/watch?v=jihxqg3kr-g
'''
print __doc__

arasharchor

Storytelling from Space: Tools/Resources

This list of resources is all about acquring and processing aerial imagery. It's generally broken up in three ways: how to go about this in Photoshop/GIMP, using command-line tools, or in GIS software, depending what's most comfortable to you. Often these tools can be used in conjunction with each other.

Acquiring Landsat & MODIS

Web Interface

• USGS Earth Explorer - Browser and data access (create a login)
  http://earthexplorer.usgs.gov/

• Landsat archive

arasharchor

# "Colorizing B/W Movies with Neural Nets", 
# Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies 
# BACKGROUND: http://tinyclouds.org/colorize/
# DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4
# USAGE:
# 1. Download TensorFlow model from: http://tinyclouds.org/colorize/
# 2. Use FFMPEG or such to extract frames from video.
# 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands:
# mogrify -resize 224x224 *.jpg
# mogrify -gravity center -background black -extent 224x224 *.jpg