pratos

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

pratos

Starting notebook in remote

$ ssh remote_user@remote_host
$ cd <repository>
$ jupyter notebook --no-browser

Connecting to the remote notebook

ssh -N -f -L localhost:8888:localhost:8889 remote_user@remote_host

pratos

library(lubridate)
parse_date_time(x = paste("01-", "13-Jan", sep = ""),
+                 orders = c("d m y", "d-y-m", "m-d-y"),
+                 locale = "eng", tz="GMT")
[1] "2013-01-01 GMT"

parse_date_time(x = paste("01-", "Jan-13", sep = ""),
+                 orders = c("d m y", "d-y-m", "m-d-y"),
+                 locale = "eng", tz="GMT")

pratos

Get list of the packages installed in the system

$ dpkg --list

Remove the packages from Ubuntu

sudo apt-get --purge remove <package-name>

pratos

import numpy as np

W = np.array([(0.01,-0.05,0.1,0.05),(0.7,0.2,0.05,0.16),(0.0,-0.45,-0.2, 0.03)]) #Weights
xi = np.array([-15,22,-44,56]) #Input
b = np.array([0.0,0.2,-0.3]) #Bias

delta = 1

y = np.sum((np.dot(W,xi),b), axis=0)

pratos

import numpy as np

W = np.array([(0.01,-0.05,0.1,0.05),(0.7,0.2,0.05,0.16),(0.0,-0.45,-0.2, 0.03)]) #Weights
xi = np.array([-15,22,-44,56]) #Input
b = np.array([0.0,0.2,-0.3]) #Bias

delta = 1

y = np.sum((np.dot(W,xi),b), axis=0)

pratos

bleach==2.0.0
cycler==0.10.0
decorator==4.0.11
entrypoints==0.2.2
html5lib==0.999999999
ipykernel==4.6.1
ipython==6.0.0
ipython-genutils==0.2.0
ipywidgets==6.0.0
jedi==0.10.2

pratos

# Base image
FROM python:3.5.3-onbuild

# Updating repository sources
RUN apt-get update

# Installing cron and curl
RUN apt-get install cron -yqq \
    curl

pratos

$ lscpu
Architecture:          x86_64
CPU op-mode(s):        32-bit, 64-bit
Byte Order:            Little Endian
CPU(s):                8
On-line CPU(s) list:   0-7
Thread(s) per core:    2
Core(s) per socket:    4
Socket(s):             1
NUMA node(s):          1

pratos

(dlgpu) user@user:~$ python
Python 3.5.3 |Continuum Analytics, Inc.| (default, Mar  6 2017, 11:58:13) 
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import keras
Using TensorFlow backend.
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally