vyraun

#!/bin/bash

# usage: 
# first make the file executable
# ./word2vec-download300model.sh output-file

OUTPUT=$( wget --save-cookies cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=0B7XkCwpI5KDYNlNUTTlSS21pQmM' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/Code: \1\n/p' )
CODE=${OUTPUT##*Code: }
echo $CODE

vyraun

elementaryOS Apps and Configs

Enbale PPA support

sudo apt-get update
sudo apt-get -y install software-properties-common

Install original plank dock

vyraun

# -*- coding: utf-8 -*-
"""
Created on Sat May  3 10:21:21 2014

@author: umb
"""

import numpy as np
    
class GMM:

vyraun

# http://www.nvidia.com/download/driverResults.aspx/117079/en-us
wget http://us.download.nvidia.com/tesla/375.51/nvidia-driver-local-repo-ubuntu1604_375.51-1_amd64.deb
sudo dpkg -i nvidia-driver-local-repo-ubuntu1604_375.51-1_amd64.deb
sudo apt-get update
sudo apt-get -y install cuda-drivers

echo "Reboot required."

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# Remove anything linked to nvidia
sudo apt-get remove --purge nvidia*
sudo apt-get autoremove

# Search for your driver
apt search nvidia

# Install Driver
# add ppa graphic driver repository
sudo apt-get install software-properties-common

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# coding: utf-8
from __future__ import division

import struct
import sys
import gzip

FILE_NAME = "GoogleNews-vectors-negative300.bin.gz" # outputs GoogleNews-vectors-negative300.bin.gz.txt
MAX_VECTORS = 100000 # Top words to take
FLOAT_SIZE = 4 # 32bit float

vyraun

/*
Copyright (c) 2011 Andrei Mackenzie

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE 

vyraun

import numpy
import random
from numpy import arange
#from classification import *
from sklearn import metrics
from sklearn.datasets import fetch_mldata
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
import time

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#!/usr/bin/env python3

from PIL import Image
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub

# smooth values from point a to point b.
STEPS = 100
pt_a = np.random.normal(size=(512))

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A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

• Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
• Evaluation function tells how good the machine learning model is.
• Optimisation is the method to search for the most optimal learning model.