Skip to main content

If a student is selected in both IIT as well as in ISI which one should be preferred


Introduction:

I think I am eligible to answer this question as I am a student of ISI Bangalore and have cracked IIT also successfully. So, I stood on that path. See, in this place, you have to choose your decision carefully. First, ask yourself the following questions:

  1. how much do you love mathematics?
  2. what is your long term goal? Being a researcher or being a top-notch industrialist?

These two questions are sorting questions. If you are not dedicated enough to mathematics, and love physics, programming, and other staff also more than or equal to mathematics, then you should go for IIT. Maybe you want to become a big industrialist, then you should go to IIT for sure. In IIT, you will be doing engineering and therefore will be exposed to a lot of subjects and will learn diverse things.

Now, let's say you are a math nerd, you solved a lot of RMO and INMO level problems, want to know a lot of mathematics in your life, boss, you are welcome to ISI.

Now, you are like me, you are not a math nerd, but you love mathematics and suck in chemistry at large. You do not like to mug up things, want to try something interesting. I will refer you to come to ISI.

Now maybe you are confused because all those points do not sum up to you. So let me clearly point out all the pros for each brand.

First for ISI:

Pros:
  1. learn mathematics and statistics from world level teachers and learn to the depth.
  2. the best place for geeks and nerds. You will find people of similar minds to discuss mathematics and transfer knowledge.
  3. zero tuition fee is a big pro actually. You will not have a big burden of loan on your back to force you trying to get a job after graduation.
  4. most of the students go to do a Ph.D. abroad after either b.math and m.math. Others, who opt for placement, get packages above 12 LPA. Obviously, you will have to put a lot of sweat behind every aspect mentioned, but, a 100% placement in either industry or academic Ph.D. is the unwritten rule of ISI.
  5. domain expert knowledge: after you get out of ISI, you will get the tag of a domain expert in mathematics and statistics. This may help you get into industries or different other things as domain experts.
IIT:
Pros:
  1. a vast culture of industry-oriented growth and the most popular brand name in both industries and academics from India. The name itself will get you to opportunities which you can't get any other way.
  2. entrepreneurship cells and business clubs are one of the biggest opportunities to grow yourself in multidimensionality.
  3. again the high brand value helps you to get through multiple fellowship and internship programs. Therefore, you can go to academics also in a better chance if you are in an IIT, although it will be harder than premiers like ISI, IISC, and CMI.
  4. again, an array of highly talented teachers will teach you the subjects at high levels.

Conclusions:

Cons are that most of the industry-oriented things are not in ISI while in IIT the environment is lot less academic than ISI. Also, most streams in IIT do not have above 80% placement. So, you, my friend now choose on the prescribed points and therefore act to your one of the most important decisions.

Comments

Popular posts from this blog

Tinder bio generation with OpenAI GPT-3 API

Introduction: Recently I got access to OpenAI API beta. After a few simple experiments, I set on creating a simple test project. In this project, I will try to create good tinder bio for a specific person.  The abc of openai API playground: In the OpenAI API playground, you get a prompt, and then you can write instructions or specific text to trigger a response from the gpt-3 models. There are also a number of preset templates which loads a specific kind of prompt and let's you generate pre-prepared results. What are the models available? There are 4 models which are stable. These are: (1) curie (2) babbage (3) ada (4) da-vinci da-vinci is the strongest of them all and can perform all downstream tasks which other models can do. There are 2 other new models which openai introduced this year (2021) named da-vinci-instruct-beta and curie-instruct-beta. These instruction models are specifically built for taking in instructions. As OpenAI blog explains and also you will see in our

Can we write codes automatically with GPT-3?

 Introduction: OpenAI created and released the first versions of GPT-3 back in 2021 beginning. We wrote a few text generation articles that time and tested how to create tinder bio using GPT-3 . If you are interested to know more on what is GPT-3 or what is openai, how the server look, then read the tinder bio article. In this article, we will explore Code generation with OpenAI models.  It has been noted already in multiple blogs and exploration work, that GPT-3 can even solve leetcode problems. We will try to explore how good the OpenAI model can "code" and whether prompt tuning will improve or change those performances. Basic coding: We will try to see a few data structure coding performance by GPT-3. (a) Merge sort with python:  First with 200 words limit, it couldn't complete the Write sample code for merge sort in python.   def merge(arr, l, m, r):     n1 = m - l + 1     n2 = r- m       # create temp arrays     L = [0] * (n1)     R = [0] * (n

What is Bort?

 Introduction: Bort, is the new and more optimized version of BERT; which came out this october from amazon science. I came to know about it today while parsing amazon science's news on facebook about bort. So Bort is the newest addition to the long list of great LM models with extra-ordinary achievements.  Why is Bort important? Bort, is a model of 5.5% effective and 16% total size of the original BERT model; and is 20x faster than BERT, while being able to surpass the BERT model in 20 out of 23 tasks; to quote the abstract of the paper,  ' it obtains performance improvements of between 0 . 3% and 31%, absolute, with respect to BERT-large, on multiple public natural language understanding (NLU) benchmarks. ' So what made this achievement possible? The main idea behind creation of Bort is to go beyond the shallow depth of weight pruning, connection deletion or merely factoring the NN into different matrix factorizations and thus distilling it. While methods like knowle