u/Commercial-Kale-5271

engineering student here, decent calc and linear algebra background from continuum mechanics coursework, already comfortable with ML basics through transformers and modern architectures. want to go deep on the actual math now, not just intuition videos, real derivations, and books that build from intuition up to advanced stuff. big thing for me is actually seeing how the math applies inside the models, not just abstract theory sitting next to it. most modern models are fundamentally probabilistic (language models included) so that lens matters a lot to me.

content quality over exercises. i'd rather have a book thats amazing at explaining and deriving things with fewer problems than one thats packed with exercises but explains things poorly. if the book is light on problems i can always find sets elsewhere, but if the content itself is weak theres no fixing that.

here's my current shortlist:

stats / probability:

• All of Statistics by Wasserman

machine learning (math heavy):

• Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar
• Mathematics for Machine Learning by Deisenroth, Faisal, Ong
• The Elements of Statistical Learning by Hastie, Tibshirani, Friedman (planning to read Introduction to Statistical Learning first as the easier version)

deep learning:

• Deep Learning by Goodfellow, Bengio, Courville

is this solid or would you swap anything out. please tell me ur suggestions

engineering student here, decent calc and linear algebra background from continuum mechanics coursework, already comfortable with ML basics through transformers and modern architectures. want to go deep on the actual math now, not just intuition videos, real derivations, and books that build from intuition up to advanced stuff. big thing for me is actually seeing how the math applies inside the models, not just abstract theory sitting next to it. most modern models are fundamentally probabilistic (language models included) so that lens matters a lot to me.

content quality over exercises. i'd rather have a book thats amazing at explaining and deriving things with fewer problems than one thats packed with exercises but explains things poorly. if the book is light on problems i can always find sets elsewhere, but if the content itself is weak theres no fixing that.

here's my current shortlist:

stats / probability:

• All of Statistics by Wasserman

machine learning (math heavy):

• Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar
• Mathematics for Machine Learning by Deisenroth, Faisal, Ong
• The Elements of Statistical Learning by Hastie, Tibshirani, Friedman (planning to read Introduction to Statistical Learning first as the easier version)

deep learning:

• Deep Learning by Goodfellow, Bengio, Courville

is this solid or would you swap anything out. Please tell me ur suggestions.

engineering student here, decent calc and linear algebra background from continuum mechanics coursework, already comfortable with ML basics through transformers and modern architectures. want to go deep on the actual math now, not just intuition videos, real derivations, and books that build from intuition up to advanced stuff. big thing for me is actually seeing how the math applies inside the models, not just abstract theory sitting next to it. most modern models are fundamentally probabilistic (language models included) so that lens matters a lot to me.

content quality over exercises. i'd rather have a book thats amazing at explaining and deriving things with fewer problems than one thats packed with exercises but explains things poorly. if the book is light on problems i can always find sets elsewhere, but if the content itself is weak theres no fixing that.

here's my current shortlist:

stats / probability:

• All of Statistics by Wasserman

machine learning (math heavy):

• Foundations of Machine Learning by Mohri, Rostamizadeh, Talwalkar
• Mathematics for Machine Learning by Deisenroth, Faisal, Ong
• The Elements of Statistical Learning by Hastie, Tibshirani, Friedman (planning to read Introduction to Statistical Learning first as the easier version)

deep learning:

• Deep Learning by Goodfellow, Bengio, Courville

is this solid or would you swap anything out. Please suggest me if there are better books out there.

so the mitacs internship application just opened (deadline sept 16) and i was checking the eligible institutions list they published, but i genuinely cant find any IITs, NITs or IIITs on there. only seeing private/state/deemed colleges under AICTE.

i think this might be a new thing specifically between AICTE and mitacs, different from the older GRI which closed last year and used to let IIT/NIT students apply through a diff list. since IITs/NITs come under MoE and not AICTE, wondering if we're just not eligible for this one at all

anyone from an NIT/IIT actually got clarity on this? did confirm not eligible or found some other way in? dont wanna assume and miss the deadline if theres actually a track for us somewhere

so the mitacs internship application just opened (deadline sept 16) and i was checking the eligible institutions list they published, but i genuinely cant find any IITs, NITs or IIITs on there. only seeing private/state/deemed colleges under AICTE.

i think this might be a new thing specifically between AICTE and mitacs, different from the older GRI which closed last year and used to let IIT/NIT students apply through a diff list. since IITs/NITs come under MoE and not AICTE, wondering if we're just not eligible for this one at all

anyone from an NIT/IIT actually got clarity on this? did confirm not eligible or found some other way in? dont wanna assume and miss the deadline if theres actually a track for us somewhere, my_qualifications = NIT student , applying for ML / ai research intern

so the mitacs internship application just opened (deadline sept 16) and i was checking the eligible institutions list they published, but i genuinely cant find any IITs, NITs or IIITs on there. only seeing private/state/deemed colleges under AICTE.

i think this might be a new thing specifically between AICTE and mitacs, different from the older GRI which closed last year and used to let IIT/NIT students apply through a diff list. since IITs/NITs come under MoE and not AICTE, wondering if we're just not eligible for this one at all

anyone from an NIT/IIT actually got clarity on this? did confirm not eligible or found some other way in? dont wanna assume and miss the deadline if theres actually a track for us somewhere

Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

Hey everyone,

Trying to get a realistic picture here.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

Hey everyone,

Trying to get a realistic picture here, not validation.

Background:

my_qualifications

3rd year at a Tier-1 NIT, non-CS branch, entirely self-taught in ML/AI. Overall GPA 7.93/10, second year 8.17 . I have some work on GitHub around LLM internals, mechanistic interpretability, and retrieval systems. Nothing published, no prior research experience, no internship. Would need the internship to be fully funded as self-funding abroad is not an option.

Research interests:

Mechanistic interpretability, LLM memory systems, retrieval augmented generation, and LLM internals broadly.

What I need guidance on:

First, given non-IIT, non-CS, no publications, no prior research, only GitHub work, what is the realistic picture for funded research internships abroad? Honest experience only.

Second, for structured programs like MITACS, KAUST VSRP, OIST, ISTernship, INSAIT, SN Bose, what actually strengthens an application from someone with my profile? Is there anything beyond GPA and publications that genuinely moves the needle?

Third, how do you actually build a cold email relationship with a professor when you have no prior research output to show? What made professors actually reply to you?

Fourth, are there programs, possibly less well known, that give genuinely good research experience and are more realistic for someone without a strong conventional profile? Looking for good research environments where the application is based on potential and work rather than credentials alone.

Fifth, for someone working in AI and ML research broadly, which labs or professors outside the very well known names are actually accessible and have taken undergrad interns before?

Any honest experience, reality checks, or redirections welcome. Thanks.

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Title: Trigram Language Model :Two implementations give different loss, are they equivalent?

I am implementing a trigram character-level language model following Andrej Karpathy's makemore series. I have two implementations and I want to understand if they are mathematically equivalent or fundamentally different models.

Implementation 1 —:Direct 27x27x27 weight tensor:

W = torch.randn((27, 27, 27), requires_grad=True)

for k in range(200):
    logits = W[xs1, xs2]
    counts = logits.exp()
    probs  = counts / counts.sum(1, keepdim=True)
    loss   = -probs[torch.arange(num), ys].log().mean()
    W.grad = None
    loss.backward()
    W.data += -50 * W.grad

Here xs1 and xs2 are integer tensors of character indices. W[xs1, xs2] directly indexes into the 3D weight tensor to get logits of shape (N, 27).

Implementation 2 : Concatenated one-hot vectors with 54x27 weight matrix:

My understanding so far:W = torch.randn((54, 27), requires_grad=True)

for k in range(200):
    xenc1  = F.one_hot(xs1, num_classes=27).float()
    xenc2  = F.one_hot(xs2, num_classes=27).float()
    xenc   = torch.cat([xenc1, xenc2], dim=1)
    logits = xenc @ W
    loss   = F.cross_entropy(logits, ys)
    W.grad = None
    loss.backward()
    W.data -= 50 * W.grad.data

Implementation 1 has 27x27x27 = 19683 parameters. Every (char1, char2) pair has a completely unique and independent set of 27 weights.

Implementation 2 has 54x27 = 1458 parameters. Because of the concatenation and matrix multiply, the contribution of char1 and char2 are additive ,char1 selects rows W[0:27] and char2 selects rows W[27:54] and they are summed together.

So my understanding is these are NOT equivalent models Implementation 1 is more expressive but needs more data, Implementation 2 makes an additive assumption but generalizes better with less data.

My questions:

1. Is my understanding correct that these two models are fundamentally different and not mathematically equivalent?
2. Which one should converge to a lower loss on a small dataset like names.txt (~32k words)?
3. Is Implementation 1 essentially just a lookup table being trained with gradient descent, making it equivalent to the counting model?

Help me finding the answer!!

>genuine question for this community

every time i use claude or chatgpt i have to re-explain myself. and even their memory feature is shallow it remembers facts about me, not how i actually think.

the idea i've been sitting on is different from just "memory across sessions."

what if the system built a dynamic personal database about you over time. not just what you asked , but how you think, where you keep failing, what explanations actually worked for you, what concepts you're persistently confused about.

so overtime the database itself evolves. it starts understanding your cognitive patterns. when you ask something new it doesn't just search your history it knows you always struggle with hierarchical concepts, it knows graph analogies work better for you than math, it knows you've asked about this topic 4 times and still don't get one specific part.

the retrieval gets smarter as the database grows. the LLM gets more personalized context each time. the system literally gets better at understanding you the more you use it.

not a chatbot. not a RAG over documents. a dynamically growing cognitive profile that makes any LLM actually understand you.

does this problem resonate with anyone here or is it too niche...

genuine question for this community

every time i use claude or chatgpt i have to re-explain myself. and even their memory feature is shallow it remembers facts about me, not how i actually think.

the idea i've been sitting on is different from just "memory across sessions."

what if the system built a dynamic personal database about you over time. not just what you asked , but how you think, where you keep failing, what explanations actually worked for you, what concepts you're persistently confused about.

so overtime the database itself evolves. it starts understanding your cognitive patterns. when you ask something new it doesn't just search your history it knows you always struggle with hierarchical concepts, it knows graph analogies work better for you than math, it knows you've asked about this topic 4 times and still don't get one specific part.

the retrieval gets smarter as the database grows. the LLM gets more personalized context each time. the system literally gets better at understanding you the more you use it.

not a chatbot. not a RAG over documents. a dynamically growing cognitive profile that makes any LLM actually understand you.

does this problem resonate with anyone here or is it too niche...

genuine question for this community

every time i use claude or chatgpt i have to re-explain myself. and even their memory feature is shallow it remembers facts about me, not how i actually think.

the idea i've been sitting on is different from just "memory across sessions."

what if the system built a dynamic personal database about you over time. not just what you asked , but how you think, where you keep failing, what explanations actually worked for you, what concepts you're persistently confused about.

so overtime the database itself evolves. it starts understanding your cognitive patterns. when you ask something new it doesn't just search your history it knows you always struggle with hierarchical concepts, it knows graph analogies work better for you than math, it knows you've asked about this topic 4 times and still don't get one specific part.

the retrieval gets smarter as the database grows. the LLM gets more personalized context each time. the system literally gets better at understanding you the more you use it.

not a chatbot. not a RAG over documents. a dynamically growing cognitive profile that makes any LLM actually understand you.

does this problem resonate with anyone here or is it too niche...

genuine question for this community

every time i use claude or chatgpt i have to re-explain myself. and even their memory feature is shallow it remembers facts about me, not how i actually think.

the idea i've been sitting on is different from just "memory across sessions."

what if the system built a dynamic personal database about you over time. not just what you asked , but how you think, where you keep failing, what explanations actually worked for you, what concepts you're persistently confused about.

so overtime the database itself evolves. it starts understanding your cognitive patterns. when you ask something new it doesn't just search your history it knows you always struggle with hierarchical concepts, it knows graph analogies work better for you than math, it knows you've asked about this topic 4 times and still don't get one specific part.

the retrieval gets smarter as the database grows. the LLM gets more personalized context each time. the system literally gets better at understanding you the more you use it.

not a chatbot. not a RAG over documents. a dynamically growing cognitive profile that makes any LLM actually understand you.

does this problem resonate with anyone here or is it too niche...

genuine question for this community

every time i use claude or chatgpt i have to re-explain myself. and even their memory feature is shallow it remembers facts about me, not how i actually think.

the idea i've been sitting on is different from just "memory across sessions."

what if the system built a dynamic personal database about you over time. not just what you asked , but how you think, where you keep failing, what explanations actually worked for you, what concepts you're persistently confused about.

so overtime the database itself evolves. it starts understanding your cognitive patterns. when you ask something new it doesn't just search your history it knows you always struggle with hierarchical concepts, it knows graph analogies work better for you than math, it knows you've asked about this topic 4 times and still don't get one specific part.

the retrieval gets smarter as the database grows. the LLM gets more personalized context each time. the system literally gets better at understanding you the more you use it.

not a chatbot. not a RAG over documents. a dynamically growing cognitive profile that makes any LLM actually understand you.

does this problem resonate with anyone here or is it too niche...

okay so my situation is this

i'm a CS student, been learning seriously for a few months. got decent fundamentals python, some ML and applied concepts, backend basics. but every time i try to start a real project i hit the same wall.

i think of something, spend hours planning it, then convince myself it's either too simple, already done by some giant company, or won't impress anyone. then i drop it and look for a new idea. and repeat.

the worst part is i actually enjoy learning when i'm building. like when i'm solving a real problem with code everything clicks. but i can't get past the "is this worth building" phase.

people say just build anything. but that feels hollow too. like what's the point of building a todo app.

how did you get out of this loop. did you just force yourself to finish something bad first. or did you find an idea that genuinely excited you enough to push through.

genuinely stuck and it's frustrating because i know i can build, i just can't commit.

okay so genuine question

every time i open claude or chatgpt i have to re-explain everything. who i am, what i'm working on, where i'm stuck. and even with their "memory" feature it's super shallow. like it remembers my name not the fact that i've been struggling with the same concept for 3 weeks.

the idea i've been sitting on is basically store every interaction semantically. every doubt, every mistake, every "i don't get this." in a vector database. so next time you ask something related, the system already knows your history with that topic and answers you differently.

not a new chatbot. just the memory layer that should've existed.

my question is do you actually feel this problem? or is this too niche and i'm just building something for myself

because every time i think about it i go back and forth. feels useful then feels like claude will just add this in their next update and i'm cooked

be honest, would you use this or not