cenkbircanoglu

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

cenkbircanoglu

import torch
import torch.nn.functional as F

from models.unet.net import Net


class CAM(Net):

    def __init__(self, *args, **kwargs):
        super(CAM, self).__init__(*args, **kwargs)

cenkbircanoglu

# import config.
# You can change the default config with `make cnf="config_special.env" build`
cnf ?= config.env
include $(cnf)
export $(shell sed 's/=.*//' $(cnf))

# import deploy config
# You can change the default deploy config with `make cnf="deploy_special.env" release`
dpl ?= deploy.env
include $(dpl)

cenkbircanoglu

# -*- coding: utf-8 -*-

""" Convolutional Neural Network for MNIST dataset classification task.

References:
    Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based
    learning applied to document recognition." Proceedings of the IEEE,
    86(11):2278-2324, November 1998.

Links:

cenkbircanoglu

cenkbircanoglu

#!/bin/bash

# Source: http://toomuchdata.com/2012/06/25/how-to-install-python-2-7-3-on-centos-6-2/

# Install stuff #
#################

# Install development tools and some misc. necessary packages
yum -y groupinstall "Development tools"
yum -y install wget  # gen'l reqs

cenkbircanoglu

Homebrew build logs for gimp on Mac OS X 10.11.5
Build date: 2016-05-14 10:43:37

cenkbircanoglu

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

cenkbircanoglu

create table deps_saved_ddl
(
  deps_id serial primary key, 
  deps_view_schema varchar(255), 
  deps_view_name varchar(255), 
  deps_ddl_to_run text
);

create or replace function deps_save_and_drop_dependencies(p_view_schema varchar, p_view_name varchar) returns void as
$$

cenkbircanoglu

package net.egemsoft.rrd.elasticPaths;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.MapWritable;
import org.apache.hadoop.io.Text;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaPairRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.serializer.KryoSerializer;
import org.elasticsearch.hadoop.mr.EsInputFormat;