Zecca Lehn ZeccaLehn

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Learning Feature Representations with K-means by Adam Coates and Andrew Y. Ng
The devil is in the details: an evaluation of recent feature encoding methods by Chatfield et. al.
Emergence of Object-Selective Features in Unsupervised Feature Learning by Coates, Ng
Scaling Learning Algorithms towards AI Benjio & LeCun
A Theory of Feature Learning by Brendan van Rooyen, Robert C. Williamson

Deep Learning

Dropout: A Simple Way to Prevent Neural Networks from Overfitting by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever and Ruslan Salakhutdinov
[Understanding

	require(RCurl)
	require(jsonlite)

	myticker<-"FB"
	url.histprice<-function(x){ return(paste0("http://globalquote.morningstar.com/globalcomponent/RealtimeHistoricalStockData.ashx?ticker=",x,"&showVol=true&dtype=his&f=d&curry=USD&range=1900-1-1\|2014-10-10&isD=true&isS=true&hasF=true&ProdCode=DIRECT"))}
	url.keyratios<-function(x){return(paste0("http://financials.morningstar.com/ajax/exportKR2CSV.html?t=",x))}

	#Retrieve historical prices
	json.histprice<-getURL(url.histprice(myticker))
	json.histprice<-sub("NaN","\"NA\"",json.histprice)

	import cv2

	cap = cv2.VideoCapture(0)
	cap.set(3, 640) # WIDTH
	cap.set(4, 480) # HEIGHT

	face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
	eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_eye.xml")

	while(True):

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	#!/usr/bin/env python
	# -- coding: utf-8 --

	import os
	import re
	import time
	import json
	import logging
	import requests

	import csv
	import requests
	import bs4
	import argparse

	# parser = argparse.ArgumentParser(description='Process a list of search terms.')
	# parser.add_argument('terms', metavar='N', type=str, nargs='+',
	# help='comma separated list of terms to search for')

	# args = parser.parse_args()

Zecca Lehn ZeccaLehn

Table of Contents

lattice

ggplot2

	import requests
	from bs4 import BeautifulSoup
	import pandas as pd
	import re
	import sys

	def currentCPI:
	url = 'http://www.tradingeconomics.com/country-list/consumer-price-index-(cpi)'
	r = requests.get(url)
	soup = BeautifulSoup(r.text)