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View Install NVIDIA Driver and CUDA.md
View Pupil_Detection_Example.ipynb
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@arasharchor
arasharchor / DeepLearningFaces.md
Created Jan 15, 2017 — forked from jdsgomes/DeepLearningFaces.md
Deep Learning for Face Recognition
View DeepLearningFaces.md

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
arasharchor / draw_neural_net.py
Created Nov 15, 2016 — forked from craffel/draw_neural_net.py
Draw a neural network diagram with matplotlib!
View draw_neural_net.py
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
arasharchor / ballTracking.cpp
Created Oct 3, 2016 — forked from sm00th/ballTracking.cpp
OpenCV code for arduino balltracking project http://youtu.be/G2nHMlx8npQ
View ballTracking.cpp
#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;
View ball_tracking.cpp
#include <Servo.h>
#define MIN_Y 90
#define MAX_Y 180
#define MIN_X 0
#define MAX_X 180
Servo tilt;
Servo pan;
View maproulette-tutorial.md

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!'

maproulette

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

Step 1 - The Idea

@arasharchor
arasharchor / balltrack.py
Created Aug 22, 2016 — forked from xamox/balltrack.py
Object Tracking with SimpleCV (White Ball)
View balltrack.py
'''
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__
View storytelling_from_space.md

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

  • Landsat archive
View forward.py
# "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