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dkbradley / hidden_relationships.py
Created September 5, 2017 19:35 — forked from anonymous/hidden_relationships.py
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sb
np.seterr(divide='ignore', invalid='ignore')
# Quick way to test just a few column features
@dkbradley
dkbradley / hidden_relationships.py
Created September 5, 2017 19:35 — forked from anonymous/hidden_relationships.py
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sb
np.seterr(divide='ignore', invalid='ignore')
# Quick way to test just a few column features
@dkbradley
dkbradley / convolutional_nn_tutorial_3.R
Created August 6, 2016 17:14 — forked from mick001/convolutional_nn_tutorial_3.R
Image recognition tutorial in R using deep convolutional neural networks (MXNet package). Part 3. Full article at https://firsttimeprogrammer.blogspot.com/2016/08/image-recognition-tutorial-in-r-using.html
# Clean workspace
rm(list=ls())
# Load MXNet
require(mxnet)
# Loading data and set up
#-------------------------------------------------------------------------------
# Load train and test datasets
@dkbradley
dkbradley / convolutional_nn_tutorial_2.R
Created August 6, 2016 17:14 — forked from mick001/convolutional_nn_tutorial_2.R
Image recognition tutorial in R using deep convolutional neural networks (MXNet package). Part 2. Full article at https://firsttimeprogrammer.blogspot.com/2016/08/image-recognition-tutorial-in-r-using.html
# This script is used to resize images from 64x64 to 28x28 pixels
# Clear workspace
rm(list=ls())
# Load EBImage library
require(EBImage)
# Load data
X <- read.csv("olivetti_X.csv", header = F)
@dkbradley
dkbradley / convolutional_nn_tutorial_1.py
Created August 6, 2016 17:12 — forked from mick001/convolutional_nn_tutorial_1.py
Image recognition tutorial in R using deep convolutional neural networks (MXNet package). Part 1. Full article at https://firsttimeprogrammer.blogspot.com/2016/08/image-recognition-tutorial-in-r-using.html
# -*- coding: utf-8 -*-
# Imports
from sklearn.datasets import fetch_olivetti_faces
import numpy as np
# Download Olivetti faces dataset
olivetti = fetch_olivetti_faces()
x = olivetti.images
y = olivetti.target
@dkbradley
dkbradley / springer-free-maths-books.md
Created December 30, 2015 08:15 — forked from bishboria/springer-free-maths-books.md
Springer have made a bunch of books available for free, here are the direct links