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vikasing.com

vikasing

Word vectors using LSA, Part - 2. Is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. [1]. More about LSA can be found here. LSA uses Singular Value Decomposition (SVD), a matrix factorization method. For a given matrix A. SVD (A) = U*S*V. In the current scenario matrix A. Is a term-document matrix ( m. Documents). Visually SVD looks like this:. Image Source: research.facebook.com. I kept the document ...

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CONTACTS AT VIKASING.COM

Vikash Singh

D-202●●●●● Hall

IIT ●●●●gpur

Kha●●●pur , West Bengal, 721302

IN

91.9●●●●9105
vi●●●●●●@gmail.com

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Vikash Singh

D-202●●●●● Hall

IIT ●●●●gpur

Kha●●●pur , West Bengal, 721302

IN

91.9●●●●9105
vi●●●●●●@gmail.com

View this contact

Vikash Singh

D-202●●●●● Hall

IIT ●●●●gpur

Kha●●●pur , West Bengal, 721302

IN

91.9●●●●9105
vi●●●●●●@gmail.com

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Word vectors using LSA, Part - 2. Is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. [1]. More about LSA can be found here. LSA uses Singular Value Decomposition (SVD), a matrix factorization method. For a given matrix A. SVD (A) = U*S*V. In the current scenario matrix A. Is a term-document matrix ( m. Documents). Visually SVD looks like this:. Image Source: research.facebook.com. I kept the document ...
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vikasing | vikasing.com Reviews

https://vikasing.com

Word vectors using LSA, Part - 2. Is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. [1]. More about LSA can be found here. LSA uses Singular Value Decomposition (SVD), a matrix factorization method. For a given matrix A. SVD (A) = U*S*V. In the current scenario matrix A. Is a term-document matrix ( m. Documents). Visually SVD looks like this:. Image Source: research.facebook.com. I kept the document ...

INTERNAL PAGES

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1

vikasing: HDMI monitor blinking (flickering)

http://www.vikasing.com/2014/11/hdmi-monitor-blinking-flickering.html

HDMI monitor blinking (flickering). Somewhere it was also mentioned that it might have something to do with "ground loop", you can read more about it on Wikipedia. Then I realized I had recently changed the power cords for the HDMI monitor, and the power cord which was in use did not have an earthing pin (3rd big pin in a plug), it looked like this:. Subscribe to: Post Comments (Atom). HDMI monitor blinking (flickering).

2

vikasing: Side Projects

http://www.vikasing.com/2015/02/side-projects.html

I started my professional career in July 2008, fresh out of college I was really excited about working on real projects. After two months of training I was assigned to the Call Handling (IVR). Team, a .Net based project. Soon I realized that the project did not require much coding, whole day went into writing test cases and IVR workflows in XML. This motivated me to work on the following side projects:. Soon after 2008 Mumbai attacks. After shutting down indews.in, I started a project called 9am. The URL...

3

vikasing: August 2014

http://www.vikasing.com/2014_08_01_archive.html

Install aura on ArchLinux without haskell dependencies. Here is how to install Aura. Package manager on ArchLinux:. Download this https:/ aur.archlinux.org/cgit/aur.git/plain/PKGBUILD? File and save as PKGBUILD in a directory. Install the missing dependencies using pacman e.g. sudo pacman -S ghc abs. This will generate a bin file. Run command sudo pacman -U aura-bin-1.x.x.x-x-x86 64.pkg.tar.xz,. Few days with Arch Linux. Over a year ago I wrote a post called Ubuntu Fail. So I decided to replace my Ubuntu...

4

vikasing: Word vectors (word2vec) on named entities and phrases - I

http://www.vikasing.com/2015/03/word-vectors-word2vec-on-named-entities.html

Word vectors (word2vec) on named entities and phrases - I. Word2vec is a C lib to compute the vector representation of a given word (or a phrase). It was released by a few Googlers and being maintained at word2vec. A couple of nice articles on what word2vec is capable of (roughly):. Deep Learning, NLP, and Representations. A Word is Worth a Thousand Vectors. Word vectors can boost performance of many ML and NLP applications, for example, sentiment analysis, recommendations, chat threading etc. Groundwate...

5

vikasing: General Problems encountered when parsing RSS or ATOM feeds

http://www.vikasing.com/2009/11/general-problems-encountered-when.html

General Problems encountered when parsing RSS or ATOM feeds. RSS and ATOM are XML specifications for web syndication. Completely depends on RSS/ATOM feeds discovered on the web, it uses Argotic Syndication Framework. Net library for parsing the feed, it is able to do the job most of the time, but whenever it encounters an unwanted element in the feed it throws an exception, which is expected. But my question is why these unwanted elements are there at first place? Title Liftoff News /title. Description T...

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August | 2012 | singularity

https://endex.wordpress.com/2012/08

Uarr; Grab this Headline Animator. Semantic Web: Part I – Introduction. Windows 7: First Look. Life in a Non-Metro: Weekdays. PITA – 1 : Couldn’t find constant VHOST. I own a domain now and now my blog can be located @ http:/ www.vikasing.com. Fb: http:/ fb.com/vikasing. Thanks for the visiting,. August 18, 2012. August 19, 2012. Blog at WordPress.com. Create a free website or blog at WordPress.com. Follow “singularity”. Get every new post delivered to your Inbox. Build a website with WordPress.com.

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vikasing.com vikasing.com

vikasing

Word vectors using LSA, Part - 2. Is a theory and method for extracting and representing the contextual-usage meaning of words by statistical computations applied to a large corpus of text. [1]. More about LSA can be found here. LSA uses Singular Value Decomposition (SVD), a matrix factorization method. For a given matrix A. SVD (A) = U*S*V. In the current scenario matrix A. Is a term-document matrix ( m. Documents). Visually SVD looks like this:. Image Source: research.facebook.com. I kept the document ...

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