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filename = 'twins';

OUTPUT = 'C:\\Users\\huy\\Desktop\\matlab\\';
TRAIN = 'C:\\Users\\huy\\Desktop\\matlab\\faces\\';
INPUT = 'C:\\Users\\huy\\Desktop\\matlab\\';

% load the training data set (60 faces)
load('data60_256.mat');

% read in original image, face and eyes coordinates/sizes data (from OpenCV's

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'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/

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Movies Recommendation:

• MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
• Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
• Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
• Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

• Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

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Transforming Code into Beautiful, Idiomatic Python

Notes from Raymond Hettinger's talk at pycon US 2013 video, slides.

The code examples and direct quotes are all from Raymond's talk. I've reproduced them here for my own edification and the hopes that others will find them as handy as I have!

Looping over a range of numbers

for i in [0, 1, 2, 3, 4, 5]:

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clear all;
clc;

% loading Preprocessed_2s, which contains data
load('Preprocessed_response_2s.mat')
% loading correct respose indices 
load('etc\response_fld0.05_correct.mat');
% load('etc\response_correct_fld0.05.mat');

S=[];

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clear all;
clc;

% loading Preprocessed_2s, which contains data
load('Preprocessed_response_2s.mat')
% loading correct respose indices 
load('etc\response_fld0.05_correct.mat');
% load('etc\response_correct_fld0.05.mat');

S=[];

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clear all;
clc;
stype={'char','pos','bar','pie'}; %modified here
load('S2.mat')  %S2.mat is the correct response data w.r.t. response_fld0.05_correct.mat
eegdata=S;
for type=1:4
%     load('processed_data\features2_normalized.mat');
%     load('processed_data\features_mRMR_400mid.mat');
%     load('processed_data\processed_target_2s1.mat');      
%     load('processed_data\features_target_final.mat');

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load('processed_target_response_2s.mat')
load('S1.mat')
load('etc\response_correct_fld0.05.mat')

nback_target = nback_target(idx);
ctype_target = ctype_target(idx);
processed_meta_2s = processed_meta_2s(idx,:);
eegdata = S;

save data1470.mat eegdata nback_target ctype_target processed_meta_2s

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How to use Pelican on GitHub Pages

Author: Josef Jezek

Install on Ubuntu

Installing Python tools

sudo apt-get install python-setuptools

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'''Trains a simple convnet on the MNIST dataset.

Gets to 98.97% test accuracy after 12 epochs
'''

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten