What's it like to intern at an Indian startup - from an IIT student
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What's it like to intern at an Indian startup - from an IIT student
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Re: What's it like to intern at an Indian startup - from an IIT student
#2I'm digressing a bit here, but with regard to the last paragraph:
> we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and achieved 62% accuracy on the classification (awesome!). although THAT meaning might seem less apparent from the conversation above :P
Isn't 62% ridiculously bad? I would expect a naive Bayesian classifier trained on first names to do much better than that. Am I missing something here?
Re: What's it like to intern at an Indian startup - from an IIT student
#3I'm digressing a bit here, but with regard to the last paragraph: > we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and a…
Yes, it is really bad. Turns out, the test data we were using was not clean and correctly categorized and that's why such low accuracy.
Re: What's it like to intern at an Indian startup - from an IIT student
#4I'm digressing a bit here, but with regard to the last paragraph: > we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and a…
I guess they meant. They took a random data set and could classify 62% of the names in this data set. The rest, owing to insufficient size of the training data, could not be classified as either.
Re: What's it like to intern at an Indian startup - from an IIT student
#5I'm digressing a bit here, but with regard to the last paragraph: > we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and a…
Hey, I'm nihit. I wrote the post so let me specify. Sorry I didn't write it properly. What I meant to say was that after training the classifier, we ran it on a random data set of names and were able to classify 62% of them. Rest remained unclassified. We had a limited training set and are expanding it currently so hopefully it will get much better. Thanks for pointing it out.
Re: What's it like to intern at an Indian startup - from an IIT student
#6I'm digressing a bit here, but with regard to the last paragraph: > we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and a…
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Re: What's it like to intern at an Indian startup - from an IIT student
#7I'm digressing a bit here, but with regard to the last paragraph: > we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and a…
Yes, it is really bad. Turns out, the test data we were using was not clean and correctly categorized and that's why such low accuracy.
The para now, is different. Did you edit it? Its always good etiquette to write that this is an edit. Good luck with the names, and the girls.
Re: What's it like to intern at an Indian startup - from an IIT student
#8I was recruited as a fresher at Infosys and had to leave the company after training in Dec. 2008 due to recession starting to set in by the time i completed my 6 months training.
I thought finding a new job would be very hard as the market was not so good but to my surprise, i found a new job within 1 month and started working at an Ahmedabad based startup as an iPhone Developer and now after 2 years, i have developed more than 15 iPhone apps and have learnt enough at my company to start my own.
It was painful to leave Infosys then but looking back, i can say that it is the best thing that has happened to better my career. My friends at Infosys who joined along with me now have mixed reactions about the company as not all of them got into desirable roles and many ended up doing support work and they often tell me that you got out of the company at the right time.
So yes, from my own experience, i can say that a startup is a much better place to work even if the salary is low (the learning experience more than makes up for it) than some large MNC where you wont even know how the module you worked on is being used (or not used at all).
Re: What's it like to intern at an Indian startup - from an IIT student
#9I'm digressing a bit here, but with regard to the last paragraph: > we were writing a gender classifier to categorize people as males/females based on first name and last name (this forms an important part of any social media monitoring product). The most common way to achieve this is through Machine Learning approaches. Gunaa tested his algorithm on a random data set (~22000 unique names if I recall correctly) and a…
he has definitely given a lower bound for the problem at hand. i'm sure if he had known which country do the "dataset girls" belong, precision would've been much better.