Hi Friends,

Even as I launch this today ( my 80th Birthday ), I realize that there is yet so much to say and do. There is just no time to look back, no time to wonder,"Will anyone read these pages?"

With regards,
Hemen Parekh
27 June 2013

Now as I approach my 90th birthday ( 27 June 2023 ) , I invite you to visit my Digital Avatar ( www.hemenparekh.ai ) – and continue chatting with me , even when I am no more here physically

Saturday 8 July 2017

RE: Can NeuralNet Shell help ?


Akshay :

You are best placed to decide which technology to incorporate into tool ( PREDICTOR ), for developing an INDEX of CULTURAL FIT , between a given candidate and the Client Company

I hope , you are making good progress and would be ready to deploy PREDICTOR on our server , in 2 weeks



Warm Regards,

Hemen Parekh
+91 98675 50808

Mitchelle :

In one of my earlier mail , I had suggested an alternative . This consisted of our preparing a list of “ Keywords / Phrases “ , against which , a clent can “ tick / rate “ , to tell us :

“ This is our Corporate Culture “

Obviously , this ( filling in / ticking ) need to be done by our client , ONLY ONCE ( - not for each position ) – since CORPORATE CULTURE does not change from position to position !

Do you think we can get our clients to do this ? Even by sitting across their table and helping them out ?

This may not be too difficult

What is . however , difficult is to decide :

“  This keyword ( or phrase ) found in a candidate’s resume = this keyword ( or phrase ) of a Client’s culture List “ – and by X %

You may want to discuss with Akshay , how best this “ Correspondence / Relation “ can be established

Can software do this from some “ first principles / rules “ ?

OR,

Can Akshay develop a simple MAPPING TOOL and give us , where , we can get our Recruiters to carry out the mapping , manually ?

In this later case , if our consultants carry out such mapping for 1,000 resumes , that might be enough starting data for the algorithm to improve upon later



From: Akshay Sehgal [mailto:akshay.s@ipredictt.com]
Sent: 05 July 2017 16:22
To: Hemen Parekh
Cc: Rohit Verma; Mitchelle; Nirmit Parekh; Hemant Juvekar; Prashant Sharma; Pradeep Bhalla
Subject: Re: Can NeuralNet Shell help ?

Hi Hemen, 

A neuralnet based model will always help since they are supposed to be the most accurate models currently. There is, however, a catch. The amount of time it takes to build a complex neuralnet to solve a problem (not to mention the amount of expertise it takes) is mind boggling. 

To give you some context, neural networks were built by hackathon participants to "calculate the number of sea lions in any given picture". This was for understanding the population of sea lions and their migration patterns for environment protection research purposes. It took the best of the minds on this planet about 1 year to build the model that, if given a picture of sea lions, can calculate the number of sea lions accurately (with almost 99% accuracy). Interestingly the winning team comprised of 4 data science PHDs from China.

Also, feeding a model the ''corpus'' of 10 stories will not be enough. It needs to provide the outcomes/predictions along with the corpus as well. So, if I want to say that a given sentence in the story talks about "Honor", I need to tag the set of words and sentences to the result "Honor".

There is a different approach to semantic analysis like you have described in the use case above. There are massive text libraries on NLTK which comprise of books, novels, scriptures, presidential speeches etc, where each word/sentence is tagged by "Part of Speech tagging, POS tagging" (which tags nouns, verbs, adjectives etc) as well as a thesaurus (meanings of each word). The combination of these lets us use natural language processing algorithms to learn from such databases and utilize these learnings to handle fresh documents of text. This doesn't require CNN but more simply just relies on highly optimized linear algebra based algorithms to score and match words/sentences on their meaning.

Thanks,
Akshay.
Co-Founder, iPredictt Data Science Labs
Head, Product Strategy & Research
+91-991-659-4778 | Skype : akshaysehgal2005 | http://ipredictt.com/


On Wed, Jul 5, 2017 at 8:51 AM, Hemen Parekh <hcp@recruitguru.com> wrote:
Akshay / Rohit :

In my mail yesterday , I talked about CULTURE FIT between a candidate and the hiring company

In this connection , I drew your attention to 41 episodes ( stories ) listed at :


Out of these , I had highlighted some 8 / 10 , leaving out 30 +

( altogether , I can send to you approx. 71 stories ; soft copies )

Here is a thought :

Using my ( human ) intelligence , I tried to find / locate , in those texts , Words / Sentences , which in my opinion , “ described “ some values of that organization ( L&T ), and highlighted in RED

Then , at the bottom of each story , I listed “ words / phrases “ , describing those VALUES ( culture of L&T ) – in Yellow

Let us treat these 10 stories ( along with those RED & YELLOW words / sentences ) as some kind of SEED

Can we feed these in a NeuralNet software and ask it to search / locate and highlight other “ words / sentences “ from the remaining 30 stories , using forward propagation ?


If that works out , we could have that MASTER LIST of narratives ( words / sentences ) , which describe Corporate Culture ( RED ) !

Thereafter we can develop a simple MAPPING TOOL , where , against each RED , I can enter a YELLOW !

Is this feasible ?


With Regards,

hemen  parekh 
( M ) +91 - 98,67,55,08,08



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