If you guys are into deep learning and always dream that if you have your own computer with GPU which helps you to run all deep learning programs. In that case I want to share the configuration which I have bought, to make my own.
I will provide you the configuration and it cost me $1650. Here is the configure.
INTEL I5 7700K CPU BOX
8 GB RAM DDR4 VENGENACE (8GB X 2)= 16 GB RAM
SSD 850 EVO 250GB
1 TB SATA HDD
GEFORCEGTX 1060 6GB
BITFENIX PRODIGY CABINET
22″ LED MONITOR WITH VGA DVI HDMI GW2270HM
650 W power supply COOLER MASTER
Cooler Master Hyper 212 X heat sink
HP COMBO C2500 DESKTOP J8F15AA
This configuration works well for me. I have used most of the available deep learning libraries on GPU mode and it works really well.
If you are planning to buy machine which can be used for deep learning then you should defiantly consider this configuration. By the way I’m using this since last two months. It is really amazing experience.
Using oracle virtual box or VMware you can install Ubuntu and run it on windows.see this video
See this link which is useful when you are trying to install Ubuntu along with Windows OS
For windows user who don’t want to install Ubuntu and want to learn or play around with Linux based OS then they should try Cygwin
How to open terminal
Open the Dash by clicking the Ubuntu icon in the upper-left, type “terminal”, and select the Terminal application from the results that appear.
Hit the keyboard shortcut Ctrl – Alt + T .
On your terminal you can see user name. In my case it is jalaj.
When you open terminal you are at system’s home location.Here ~ stands for system’s home path and your system’s home path will be /home/yourusername
So /home/jalaj is same as ~ (which is useful to understand basic commands)
To see you are currently at which location then use following command.
when you open File explorer or Folders you can see Home icon on your left side which shows you are at system’s home location
When you press Ctrl + l on title bar you can see the full path of current system location
Make directory or folder
$ mkdir /home/yoursystemusername/test
or you can do it by following this
$ mkdir ~/test
see the following picture
Change directory or jump from one location to another
$ cd /home/jalaj/test
$ cd ~/test
List down directories & files
$ ls # This is for list down all directories
Command with flags
list all files including hidden file starting with ‘.’
list directories – with ‘ */’
list file’s inode index number
list with long format – show permissions
list long format including hidden files
list long format with readable file size
list with long format with file size
list in reverse order
list recursively directory tree
list file size
sort by file size
sort by time & date
sort by extension name
# List directory using relative path
$ ls ~/test
# List directory using absolute path
$ ls /home/yoursystemusername/test
# List root directory
$ ls /
# List parent directory
$ ls ..
# List user's home directory means /home/yoursystemusername
$ ls ~
# List with long format
$ ls -l
# Show hidden files
$ ls -a
# List with long format and show hidden files
$ ls -la
# Sort by date/time
$ ls -t
# Sort by file size
$ ls -S
# List all sub-directories
$ ls *
# Recursive directory tree list
$ ls -R
# List only text files with wildcard
$ ls *.txt
# List directories only
$ ls -d */
# List files and directories with full path
$ ls -d $PWD/*
# List files and directories with permissions in reverse order
$ ls -ltr
Remove directory or files
# delete file
$ rm /home/jalaj/test/test.txt
# Forcefully delete write-protected file
$ rm -f /home/jalaj/test/test.txt
# If you are already in ~/test directory then
$ rm -f ./test.txt # Current directory is referred as ./
# If remove directory
$ rm /home/jalaj/test/demo
# remove directory recursively
$ rm -r /home/jalaj/test
# remove directory recursively and forcefully
$ rm -rf /home/jalaj/test
# Remove all files in the working directory.
# rm will prompt you for any reason before deleting them.
$ rm -i *
Copy files or directory
$ cp FLAG SOURCE DESTINATION
force copy by removing the destination file if needed
interactive – ask before overwrite
link files instead of copy
follow symbolic links
no file overwrite
recursive copy (including hidden files)
update – copy when source is newer than dest
verbose – print informative messages
# Copy single file main.c to destination directory bak
$ cp main.c bak
# Copy 2 files main.c and def.h to destination absolute path directory
$ cp main.c def.h /home/jalaj/test/
# Copy all C files in current directory to subdirectory bak
$ cp *.c bak
# Copy directory src to absolute path directory /home/jalaj/test/
$ cp src /home/jalaj/test/
# Copy all files and directories in dev recursively to subdirectory bak
$ cp -R dev bak
# Force file copy to directoy
$ cp -f test.txt bak
# Interactive prompt before file overwrite
$ cp -i test.c bak
cp: overwrite 'bak/test.c'? y
# Update all files in current directory
# - copy only newer files to destination directory bak
$ cp -u * bak
Move files or directory
$ mv FLAG SOURCE DESTINATION
force move by overwriting destination file without prompt
interactive prompt before overwrite
update – move when source is newer than destination
verbose – print source and destination files
# Move main.c def.h files to /home/jalaj/test/ directory
$ mv main.c def.h /home/jalaj/test
# Move all C files in current directory to subdirectory bak
$ mv *.c bak
# Move all files in subdirectory bak to current directory
$ mv bak/* .
# Rename file main.c to main.bak
$ mv main.c main.bak
# Rename directory bak to bak2:
$ mv bak bak2
# Update - move when main.c is newer:
$ mv -u main.c bak
# Move main.c and prompt before overwrite bak/main.c
$ mv -v main.c bak
'bak/main.c' - 'bak/main.c'
Data science creates lot of buzz since past few years. There are so many questions come into our mind when we heard the term data science such as Why this field creates a lot of buzz , What kind of Data is needed for Data science,What are the important aspects of the data science , What are the applications of Data science, What are the techniques available to solve data science related problems, How can anybody can getting into the data science.etc… let’s check out all the questions related to data science.
What is Data Science?
Let’s go back in 1990, when world wide web is evolved , slowly and gradually people are using this powerful invention from last 25 years and making it batter.
The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race on web.
Now a days world wide web is the major resources of the data.People from all over the world using web everyday .This usage generate lot and lots of data. According to the report on EMC, In 2013 , we have 4.4 zettabytes (ZB) of data on web.This ZB of data contains historical data as well as real time data. Everyday we are interacting with data whether its social media, web search,news,blogs,videos,images,documents etc..
Now we have more then enough data which can be used for extracting knowledge out of it. After analysing the data by using proper scientific techniques we can find some of the hidden pattern or facts from the data which will lead us to solve existing unsolvable questions.
“This scientific way of analysing data or extracting knowledge out of data is called Data science.”
“Data science is all about making sense out of the data or extracting the knowledge from the data using data science techniques.”
What kind of Data is needed for Data science?
There are three kind of data available on web,
This kind of data is highly organised. Data is stored in table
A data model explicitly determines the structure of data.
This kind of data has relational key and they are stored in relational databases.
Examples: Student information database,Employee information database, etc..
Semi-structured data is a form of structured data but it is not completely similar to the structured data.
It contains tags ,other markers or key-value pairs to separate semantic elements and enforce hierarchies of records and fields within the data. Therefore, it is also known as self-describing structure.
Examples : XML, Json, CSV
Majorly on web we find data which does not follow any structure.
This kind of data is not neatly fit in to the traditional relational databases.
Examples: Satellite images, Scientific data, Photos, Videos, Radar data, Mobile data, Text , web content,Social media etc…
Majorly semi-structure and unstructured data set is used for solving data science related problems. There are very small set of applications in which structured data can be used.
Why does data science field create a lot of buzz?
In current era, We have lot of data, cheap but efficient hardware, tools and techniques which emerging in last few years to solve the previously unsolvable questions , these are the factors which create buzz around the data science.
Aspects of the Data Science
Data science is umbrella term, this field contains many other fields in it.
Data science includes Statistics, Programming, Machine Learning, Natural Language Processing(NLP), Text Mining, Visualisation, Big Data, Data Ingestion, Data Munging, Tools for data science.
Data science techniques
Data science techniques majorly include statistics, Machine learning and Deep Learning for solving problems like speech recognition, Image recognition, various NLP applications, etc..
Data science tool kit
Those who are coming from the technical background can use following tools
Scripting language for rapid prototyping (Scala or Python)
R – Statistics programming tool
Deep Learning libraries tenserflow, torch, Deeplearning4j etc…
Social media libraries
Basic Machine learning libraries
Those who are coming from the non-technical background can use following tools
Google cloud prediction API
Internet Search – Ranking algorithms
Digital advertisement – Statistics techniques heavily used
Recommend system -Machine learning techniques majorly used
Image recognition – Deep Neural Network /Deep and wide Neural Network
Speech recognition – Deep Neural Network /Deep and wide Neural Network/Linguistics techniques
Gaming – Machine learning / Deep Neural Network /Deep and wide Neural Network
Credit risk modelling – Statistics and Machine learning
Fraud detection – Statistics, Machine learning and graph theory
Social Media Intelligence – NLP, Sentiment analysis, Influence detection, etc..
Intelligent Chat bots – Statistics, Machine learning, NLP and deep learning
Self driving car -Rule based system
Robots – under research
From next post onward, I am going to start tutorial series for data science beginners.
This tutorial series includes
Ubuntu for beginners
Tool kit list and installation guide
Regular expression guide
Scraping of the data
Data cleaning / pre-processing
Basics of statistics
Basics of Machine learning techniques
Apply machine learning techniques on pre-processed data