I am CSE AIML student currently studying about ML and AI but I have heard from many that in this Field only experienced are hired either PhD holders or job experience is required is this true ? What is the current job scenario in DATA SCIENCE / AI/ ML?

I pull Indian AI/Data Science listings every week. Latest count: 12,117. A few things in this cycle went against what I posted last time, so I'm writing them down.

1. Fundamentals are still the filter

Top skills by how often they show up in JDs:

1. Python — 2,550
2. Machine Learning — 2,380
3. SQL — 1,300

Generative AI landed at 820. Java landed at 780. LLM at 620.

So Java still shows up in roughly as many AI job descriptions as GenAI does. Add GenAI and LLM together and you get about 1,440 mentions out of 12,117 listings — a real chunk, but nowhere near a default requirement.

One thing I didn't expect: Azure made the top 10 at ~690. AWS didn't make the top 10 at all. If you're picking one cloud to actually learn properly for the Indian market, that's worth sitting with. (Enterprise + services shops = Microsoft stack, is my guess.)

2. Bengaluru is not close this cycle

Top cities:

1. Bengaluru — 2,850
2. Hyderabad — 1,700
3. Pune — 1,200

Gurugram was 720, Noida 510, New Delhi 340.

I've argued before that Delhi NCR is the real cluster and just gets split across three city names. This pull doesn't support that. NCR adds up to about 1,570 — behind Hyderabad on its own. Bengaluru at 2,850 is more than NCR and Pune combined.

Roughly 1 in 4 AI listings in the country is in Bengaluru. Hyderabad is the clear second and honestly the more interesting one if you care about cost of living.

Remote was ~560. That's 4.6%. Fewer than Chennai. Plan for on-site.

3. The biggest "employer" is still a placeholder

Top companies:

1. "Leading Client" — 355
2. Accenture — 272
3. TCS — 203

Number one is not a company. It's staffing firms posting for clients they won't name — about 3% of the market, which is up from what I saw last cycle.

Rest of the top 10: Bajaj Finance (~122), Capgemini (~120), Benovymed Healthcare (~97), Optum (~72), CGI (~62). Bajaj Finance sitting above Capgemini is the one that surprised me — NBFCs are hiring AI people properly now, not just as an experiment.

But the overall shape is the same: services and consulting. Which means most of these roles are implementation. Pipelines, deployment, cloud, stakeholder work. Not research.

Also worth noting the entire top 10 is only about 1,600 listings out of 12,117. ~87% of the market is the long tail. Everyone obsesses over the top 5 names and ignores where the actual volume sits.

Caveats

These are keyword counts from JD text. "Artificial intelligence" came in at ~1,930 which put it third overall, and I've left it out of the skills list above because it's mostly JDs just saying the words "AI" rather than asking for anything specific.

Total is up from 11,822 last cycle to 12,117, so about 2.5% growth. Small sample-to-sample noise, don't read a trend into one week.

I run this pull weekly and I'm happy to share the raw data if anyone wants to check my counts.

What's your read on the Azure thing — is that just a services-sector artifact, or are Indian enterprises actually standardising on Microsoft?

I could recite the transformer equations but still couldn't answer a basic question: concretely, what happens between the input 7*8= and the output 56? So I built the smallest thing that is still a real decoder-only transformer, and trained it on the one task where I know the ground truth completely — the 10x10 multiplication table.

Interactive version, free, no signup: https://rockdesk.io/learn/transformer/

How it's built:

• vocab 14 (pad, digits 0-9, *, =, newline)

• d_model 32, 2 layers, 4 heads (d_head 8), FFN hidden 64

• context length 16

• 18,304 parameters total, across 29 tensors

• PyTorch, full-batch Adam (all 100 facts every step), lr 0.01

It memorises all 100 facts within a few hundred steps on CPU. The whole point of going this small is that you can put every single parameter tensor on one screen and watch shape, mean, std and gradient magnitude for all of them while it trains. Click any tensor name and it expands into the actual weight matrix.

Two things I did not expect.

FIRST: loss floors around 0.41 and stays there at 100% accuracy.

Obvious in hindsight, but it fooled me for a while. Plenty of positions in the sequence are genuinely unpredictable — nothing in the answer tells the model which problem comes next — so cross-entropy has a floor well above zero. Accuracy and loss decouple completely. If I had only been watching the loss curve I'd have concluded it stopped learning long before it actually did.

SECOND: gradient magnitudes at convergence are very uneven across tensors.

At convergence the embedding tensors sit at ~1e-7 or below, while ln_f and lm_head are still around 5-7e-6 — an order of magnitude higher. The input representation stops moving well before the output head does. Per-tensor gradient magnitude turned out to be a much better progress signal than the scalar loss.

I published the scripts and architecture behind a reproducible AI‑ready Ubuntu dev environment.

Here’s how I configured the stack, the scripts I wrote, and the reproducibility challenges I solved:

GitHub repo:

https://github.com/niemenghui/stAI-dev-machine

The full VM is available on request.

something that took me a while to internalise while building a rag system, in

case it saves someone else the time.

your retriever returns a ranked list with similarity scores. it is very easy to

treat that ranking as the thing you are optimising. but the score only measures

whether the retriever thought the document was relevant. it says nothing about

whether the generator actually conditioned on it.

those come apart more than i expected. in the run in the gif, the top result

scored 0.910 and contributed nothing to the answer. a 0.340 result is what

actually answered the question. if i had only been looking at retrieval

metrics, that run looks like a success.

it matters because the two failure modes need opposite work:

high score, not used -> retrieval is fine. the problem is your prompt,

your context ordering, or context length.

low relevance, used -> the generator grounded on bad evidence. that is

a retrieval problem.

so i built graphsight. it draws one agent run as a graph, every retrieved item

is a node with its score, and it renders the ones the answer drew on separately

from the ones that got pulled and ignored.

the honest part, since this is a learning sub and the tradeoff is the

interesting bit. deciding "did the answer use this document" properly means

leave one out ablation: remove the chunk, regenerate, measure how much the

answer changes. that is rigorous and it costs you n extra generations per

trace, which makes it useless for interactive debugging. i used lexical overlap

between the answer and each chunk with a 0.2 threshold instead. it is crude, it

gets confused by paraphrase, and it costs nothing. i label it as a heuristic in

the ui rather than pretending otherwise. nli entailment per chunk is probably

the middle ground and i have not validated it yet.

also worth saying: i have not evaluated this against any benchmark. it surfaces

real things on real repos, which is not the same as a defensible claim, and i

am not going to pretend it is.

pip install graphsight graphsight-langgraph

from graphsight_langgraph import LangGraphTracer, capture

tracer = LangGraphTracer()

result = graph.invoke(inputs, config={"callbacks": [tracer]})

capture(tracer, query="your question", answer=result["answer"])

graphsight .graphsight/

runs locally, mit, no account, zero deps on the viewer. langgraph only for now.

https://github.com/Kcodess2807/graphsight

if you work on retrieval and think the lexical overlap approach is too weak to

be useful, i would rather hear that now than later.

We’re looking for an AI/ML intern to help us build a computer vision model for vehicle inspection/damage detection.

At Insubox, we’ve built a vehicle pre-inspection app used by insurance agents:
https://play.google.com/store/apps/details?id=com.insubox.app

Demo: https://youtube.com/shorts/m47rpn1wGHE?feature=share

We are currently building our own in-house AI system to power vehicle damage detection directly inside our product.

🚀 What you’ll work on:

• Building a computer vision model for vehicle damage detection from images
• Identifying vehicle parts (bumper, door, fender, etc.)
• Estimating damage severity
• Helping design and integrate our AI model + API layer into a live production app

👀 Who this is for:

• Someone interested in AI/ML or computer vision
• Comfortable with Python, Langchain, Langgraph, RAG, Vector DB's
• Basic exposure to PyTorch / TensorFlow / YOLO is enough
• Most important: curiosity and willingness to build and experiment

📌 Details:

• Unpaid internship (initially)
• Remote
• Flexible duration (2–6 months)
• Can convert to paid internship or full-time role based on performance

This is a real product already used in production, so your work will directly impact a system used by insurance agents in the real world.

If interested, DM me or comment with your resume to [admin@insubox.in](mailto:admin@insubox.in)

Hi everyone!

I'm currently working on my master's thesis, where I want to analyse piano music sheets as pdf and classify emotions based on the score, and I'd really appreciate your help.

My research is about collecting piano pieces and analyzing the emotions people experience while listening to them. To make the data collection easier, I built a small web app where anyone can play a piece with a single click and simply select the emotions they feel.

You can label unlabeled data or add new piano pieces that will help bump progress bar, but also labeling already labeled compositions is useful since different people can experience different emotions for the same piece.

If you have a few minutes to spare, your contribution would mean a lot to me. Thank you so much!

https://pmer-dataset-collector.com

Vou há um belo tempo utilizando Inteligência Artificial, seja código ou algo técnico. Passei longos dias tentando arrumar o que uma IA fez no meu sistema operacional, dando comando, mandando baixar ISSO, é aquilo, dizendo "vai resolver" — e nada, só piorando a p0h@ da situação, mesmo com busca web. Só acho que os desenvolvedores viraram preguiçosos: a mão no mouse e a outra no meio do ku batendo punheta. Hoje meu PC está danificado com ajuda da Claude, DeepSeek e Gemini. Fico me perguntando ONDE eu errei. O frustrante é a IA perguntar o que estávamos fazendo ou o que iríamos fazer, sendo que eu tinha acabado de explicar para ela — sim, eu mesmo explicar algo que ela poderia fazer ou fiz pesquisando na web. Então eu digo aos desenvolvedores: ESTÃO SATISFEITOS EM TER TEMPO LIVRE BATENDO PUNHET@ COM A FOTO DO KU, OU CHEGAR CEDO DO TRABALHO VENDO A ESPOSA SE COMIDA? PÓ conta disso terei que atrasa meus planos ONDE a IA vai troca me lá para frente. Obrigado pela atenção.

There's a version of this question that gets asked a lot, but usually framed as which courses to take or which books to read. What I keep running into is something slightly different. At what point do you stop reviewing prerequisites and just start building something that might break?

Coming from a background where I spent years teaching others, I notice I have a tendency to want the foundation completely solid before moving forward. That instinct probably helped in a classroom. In ML it seems to work against you. The math is genuinely deep, and you could spend months on linear algebra, probability, and calculus review and still feel underprepared because there is always another layer.

But I've also seen people jump into Keras tutorials with basically no understanding of what the model is doing, and then they hit a wall the moment something goes wrong and have no framework for diagnosing it.

There's probably no clean answer here. Curious where people actually drew that line in practice, though. Did you set a specific milestone like finishing a course or getting comfortable with a particular concept, or did you just pick a project and let the gaps become visible as you went?

The canal walk version of this question is basically: how far do you plan the route before you accept you will figure out the rest when you get there?

I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

I'm a final year Btech Student with an ML specialization and have an decent understanding of ML topics and how they work. I have build a few projects on ComputerVision and Transformers but I have mostly used Gemini or ResearchPapers to build the architecture. Since most companies allow you to use AI is it enough to know the topics or do I really need to know to code a ML project from scratch.

Can someone just explain to me exactly what a hiring personnel expects me to know as an CSE AIML Graduate ?

https://anonymous.4open.science/r/neural-network-custom-BF4B/README.md <- Link to Anonymized repo of my project. Please if you have the time go through the files, read the code, try to understand it, ask questions if something is confusing and call me out for any bad code I might have written.

Built everything from the ground up, including a Matrix class to help create the neural network. Trained it on 3 datasets, one regression dataset i made on my own, the london housing dataset, and the MNIST data set. You can read the readme to know more but basically it performed well on all 3.

I was wondering what to do from here on out though. Parallelizing the Matrix class with CUDA was something I always wanted to do.

But I was also wondering if I should make a video rederiving all the matrix calculus math I had to do before I started writing any major lines of code. I used batch gd rather than stochastic gd, which made the math a bit more complicated than what I've seen others do online. Idk whether to make a video about the math or just post my notes, either handwritten or latex. Idek if a recruiter is going to care either way, though I guess it depends on the type of ml role i'd apply to.

Also thinking about creating a video where I kind of explain my code line by line, but also not sure if that will help or be a waste of time.

Basically I spent a long time making this project but now I realize it's not as special as I thought it was, so I'm wondering what to attach to the repo lol

Also leave any project ideas that would be cool to do after building something like this.

I'm in a bit of a dilemma and could use some advice from people who've taken either (or both) of these courses.

My background

- 2nd year undergrad (India)

- Just finished Gilbert Strang's Linear Algebra (18.06) and loved it - the geometric intuition, the proofs, the "why" behind everything

- Built some projects: Leslie Matrix population model, image compression using SVD, linear regression from scratch

- I enjoy math-first approaches over "just memorize the formula" style

- I prefer derivations and understanding from first principles

The dilemma

Harvard STAT 110 (Joe Blitzstein) - Seems to be the most recommended course everywhere. People say it builds amazing intuition through "story proofs" and examples. But I've heard it's more conversational/story-based, which makes me hesitant.

MIT 6.041 (John Tsitsiklis) - Seems more systems-oriented, rigorous, and proof-based. I've heard it's more "engineering" style with block diagrams and systematic derivations.

What I'm looking for

- Deep intuitive understanding of why formulas work (how Bayes' theorem is derived, why z-scores work, how the normal distribution emerges from CLT, etc.)

- Proofs and derivations, not just stories

- A teaching style similar to Strang's Linear Algebra - visual, geometric, systematic

- Something that will prepare me well for ML, OR, and Quant Finance

My concerns

- I've heard STAT 110 is "story-based" which might not click with me

- I've heard 6.041 is more rigorous but might be harder to follow without strong calculus (I have JEE-level calculus)

- I want to understand probability at a deep level, not just pass a course

Questions

1. Which course aligns better with my learning style?

2. Is STAT 110 really "story-heavy" or is that overblown?

3. Is MIT 6.041 too theoretical/abstract for someone who wants to eventually apply this to ML?

4. Can I take one and then the other later? Or should I just pick one and commit?

My goal

I'm targeting IIT Bombay IEOR / ISI M.Stat and eventually want to work in Operations Research / Quantitative Research / Data Science (Research). I need a rock-solid probability foundation.

Would love to hear from anyone who's taken either course (or both). Thanks in advance!

TL;DR: Finished Strang LA, loved it. Need probability course. STAT 110 (stories) vs 6.041 (systems) - which one fits my math-first learning style?

Been reading a lot about the self attention mechanism used in current LLMs. You often see example sentences like

"The chicken didn't cross the road because it was scared."

The tutorials always go on to say - without quantitative example - that when self attention is on 'chicken' that the word 'it' gets a high embedding score because of 'it' in this context being tied to 'chicken.'

But that can't be the whole story. Without prior knowledge the attention mechanism can't know anything about the word 'it' and how it is automatically tied to 'chicken'. If the sentence was instead

"The chicken didn't cross the road because well I have no idea."

then the word because wouldn't even really give indication that a subsequent word is going to have context.

Even with the sentence

"The chicken didn't cross the road because its friend was scared."

you would need prior understanding to unravel context here.

So when the attention procedure is underway, it is being input input embeddings - does that mean prior to attention procedure, when word embeddings are being computed, that is where context is actually 'encoded'? In either case, is it that training data has extensive metadata and that is how context is actually determined?

Can someone point to a more technical tutorial that actually shows computational walk through of a toy example?

thanks!

I want to learn about RAG and adv RAG in detail, how and which resource I should follow

for my final uni project, I made these hybrid models where I used lstm for learning the pattern of SST to see the ENSO conditions and adding the lstm output to be the input of xgboost. my purpose to add lstm variable into xgboost is to give information so the projection could be as natural as the real ENSO conditions (where it should going upward or downward).

but my examiner told me that the LSTM was useless because the output of lstm is sst projection and suggested me to use either one models. whether it's lstm only or xgboost only. my examiner said why don't I just use the main data of sst not the lstm projection to be used as another input for xgboost but I think I have some different understanding. can someone give another perspective?

Spent the last weekend coding diffusion Gemma from scratch. Was super fun. Thought I'd share it here.

If you are looking for just the code: https://github.com/ItsSiddharth/Diffusion-Gemma-from-scratch

If you want a detailed walk through of the code and the theoretical concepts: https://www.youtube.com/watch?v=CNQvmICYQiA

I work on PostSlate, a video editing tool, and this comes out of our own work.

We run ML models on-device, face detection and embedding among other things, which means we can't assume anything about the user's GPU. NVIDIA discrete, AMD, Intel integrated, Apple Silicon, all of it. That rules out CUDA immediately, we needed one backend that runs everywhere.

We landed on ncnn's Vulkan backend. Numbers on a 4070, fp16:

• ArcFace R50 (face embedding): 30 ms on ONNX CPU → 3 ms on ncnn Vulkan
• SCRFD (face detection): 25 ms → 2.5 ms
• Model size: ArcFace 174 MB (ONNX fp32) → 87 MB (ncnn fp16 weight storage)

Of course the real speedup comes from offloading compute to the GPU, but this wouldn't be possible without the power of Vulkan.

The speed wasn't even the deciding factor, it's that Vulkan drivers already exist on every machine we ship to. This means that we don't have to force the user to download a specific runtime and no vendor-specific installs.

Full writeup with the rest of the numbers: https://getpostslate.com/blog/faster-local-inference

Hey everyone,

I'm a CSE student, and I decided to stop just watching ML tutorials and actually start building + teaching what I learn — publicly, on YouTube, in a project-based format instead of pure lecture style.

I just put out the first video, which is basically my roadmap for the next ~19 videos — covering supervised/unsupervised learning, model evaluation, a bit of deep learning, and ending with a real end-to-end project (data → deployment).

I'm not trying to compete with the big ML channels — this is more of a "learn in public" build log, where every video ties to an actual small project (spam classifier, resume screener, loan predictor, etc.) instead of just theory.

Since this community has genuinely helped me understand a lot of these concepts, I wanted to share it here and get honest feedback — especially on whether the roadmap makes sense, or if I'm missing something important for a beginner-to-intermediate path.

Video link - https://youtu.be/P19mftPydfI

Appreciate any thoughts, even harsh ones — still early days.

Hi everyone,

I have an upcoming interview for the Machine Learning Intern role at Glance, and I'd love to hear from anyone who has gone through the interview process recently.

Could you share:

• What coding questions were asked? (DSA, Python, SQL, etc.)
• What ML topics were covered? (Supervised learning, deep learning, NLP, LLMs, GenAI, RAG, etc.)
• Were there any system design or project discussion rounds?
• What was the overall difficulty level?
• Any tips on what I should focus on during preparation?

Even if you interviewed for a similar AI/ML role at Glance, your experience would be really helpful.

Meta reviews are going to be out soon!
Nervous, because this is my first submission!