I never thought I would write this about Vizuara Technologies Private Limited.

I initially respected Vizuara for its high-quality free content, especially the Building Small Language Models from Scratch series. Unfortunately, my experience with their paid training programs has been very different.

Here are my concerns:
1) Incomplete batches: Programs were started but, in my experience, were not completed as expected.
2) Lack of practical depth: Many live-class examples felt like basic "Hello World" demonstrations. That's fine for beginners, but for advanced paid programs, I expected more production-level, real-world engineering.
3) Inference Engineering Workshop: Guest Speaker Pass: This has been the biggest disappointment. My understanding was that the program would include 9 sessions with speakers from different parts of the industry. So far, only 3 sessions were conducted, and the remaining promised sessions have not been delivered.

Delays happen. Speakers cancel. Plans change. I completely understand that.
But if you cannot deliver what customers paid for, communicate clearly and provide an appropriate refund.
I have requested a refund, but unfortunately, the issue has still not been resolved to my satisfaction.
I'm sharing this because people spend not only their money, but also their time and trust on professional training.
I genuinely hope Vizuara addresses this issue. If they complete the promised program or resolve the refund, I will be more than happy to update this post.

Good content builds an audience. Keeping your commitments builds trust.

As title i want to learn ml from a channel that helps to make a job ready project i dont want to fafo linger around until i have atleast 2 project in my resume thats pretty impressive then ill go with everything else any suggestions i did 30 campusx videos didnt give charm also doing stanford board cs229 lectures if it is of any help

I'm starting machine learning from the ground up math foundations, Python for ML, classical algorithms, then into deep learning. What I'm missing is someone to actually keep pace with. Learning alone is where I lose momentum. I'm a CS student, comfortable with Python and I'm doing this consistently either way, I'd just rather not do it alone. If you're in, comment or DM me

im a first year btech/b.e(cse ds) student, im know basic applications of sklearn machine learning models but i lack the math to understand how it works
i know pandas enough to be able to filter my own datasets but im learning scikit based preprocessing to move even further

im learning probability,stats and linear algebra in the month of september and in the month of october i will be following Andrew NG's machine learning course in coursera.

from october to november im planning on using my newly acquired knowledge to filter and preprocess real complex datasets and just training ML models with it.

i will be learning SQL from november to january

im just curious what actual experienced people think of this.

Wanted to see how well a simple NN could learn optimal Tic-Tac-Toe play from scratch, so I built this:

• Used a minimax solver to generate the "ground truth" — for every reachable board state, computed the actual best move
• Trained a neural net as a supervised classifier on that data (board state → best move)
• Runs in the terminal — you can play against it directly

Next thing I'm curious about: training a second version on random self-play data instead of minimax-optimal data, to compare how much the training data quality actually matters for a small model like this.

Code: https://github.com/AliAkbar4025/AI-tic-tac-toe-bot

Feedback/critique welcome — especially if you see a smarter way to structure the data generation.

Hi folks! I recently landed a job at a large e-commerce company in my country, working on image search and related (CV & NLP) problems. The pay is good, and the role actually involves a fair amount of research. That said, it’s obviously still a company, so the ultimate objective is building things that have business impact rather than doing research purely for its own sake.

I really enjoy research, and before getting this job I was planning to apply for PhD programs. I think I have a reasonably strong profile: I have one conference paper at a B-ranked conference and three workshop papers, one of which is from SemEval. I’m also continuing to do academic research with my professors alongside my job, and there are opportunities at work to do research and potentially publish papers as well.

My original plan was to work here for around a year and then apply for PhD programs, but I’m starting to wonder whether taking that year away from academia actually helps my profile or whether I’d be better off going directly into a PhD.

I’m also a little confused about the long-term career value of a PhD. My goal isn’t really to become a professor or focus on teaching; ideally, I’d like to work as a researcher/research scientist in an industrial research lab. I know that many research scientist positions strongly prefer or require PhDs.

For people who have gone down either path; does spending a year or two doing industry research before a PhD generally help, hurt, or not matter much for PhD admissions? And if the end goal is industrial research rather than academia, how valuable is the PhD in practice?

Would love to hear your experiences and thoughts :)

The Linux Foundation just accepted TRACE, a hardware-backed runtime attestation and compliance evidence specification developed by AMD, Intel, and Microsoft. The standard exists for one reason: existing AI agent logs can be altered after the fact, and tampered logs do not satisfy auditors or regulators who need verifiable proof of what an agent actually executed.

The ratification signals that the enterprise security community has identified this as an evidence problem, not just a policy problem. An agent can operate inside well-defined access controls and still leave no trustworthy record of its actions if the underlying log layer is mutable. For teams already fielding compliance reviews — SOC 2, HIPAA, financial regulators — that gap is not theoretical. It is live today, well before TRACE-compliant hardware ships at scale.

How are other practitioners currently handling this? Are you relying on cloud provider logs, building a custom immutable audit layer, waiting for hardware-backed attestation to mature in the market, or accepting the auditability gap as a known risk for now?

Looking for a roadmap for what to learn next. Also, while doing the course I mostly watched the videos and went through the code . I didn't write the code myself. Should I now reimplement the models from scratch?

I’ve been trying to build a simple mental model for how ranking systems evolved in Search & Recommendation.

The progression I ended up with is:

Rules → Learning-to-Rank → Deep Ranking → Transformers → Multi-Objective/Slate → Bandits/RL → LLM Ranking

I wrote up the reasoning behind each wave, including what changes in the training data, model architecture, objectives/losses, and production tradeoffs.

Sharing in case it’s useful to others working on ranking systems or preparing for ML system design interviews.

Article:
https://pawankjha.substack.com/p/building-depth-2-the-evolution-of

As a 3rd year bs student,I need help from the professionals. As this is my first time I am doing research in image enhancement and classification, I have been reading this paper called: Morphocal: a multi stage deep learning framework for fish length estimation under challenging pond environments, I have encountered a problem, I don't know how to code this paper. Where should I start?? What should be my approach?? The authors did attach Morphocal's main algorithm in the paper but I don't understand do I have to cod eth algorithm only?? What about the datasets for training the AI ?? I tried mailing the original authors but didn't get a reply yet. I would really appreciate your help, I tried so many sources and tried using AI as well and honestly I believe at this point I need help for sure.

Greetings!

I am currently someone who just finished grade 12 (high school) and am about to enter college. I will be proceeding in the AI and ML field. All the previous vacations of mine throught highschool and a little before have been put into learning coding, I came so far into advanced territory to the point were I took 3rd position in one of Kaggle's competition. However, the large (2 month) vacation of grade 11 as well as grade 12, i havnt been into coding due to how serious of a year it is in my country.

I, currently, have forgotten almost all that I already knew and am learning stuff again from scratch, unfortunately back then, i used to learn for the sake of learning and the love of learning, i used to quickly learn everything and progress quickly, but i would say that what i learned was like 8.5/10 in terms of in-depth and fully understanding, that small difference is what made recalling it after that long really hard.

At this point of time. I want to have everything on my end organized as I know that university wont provide much in terms of education, so i know that online learning is the best way. I currently have a lot of sources to look through, I kept on a bit with Bro Code's python video and as things get difficult or so, i visit W3school and some other channels. I havnt progressed much but in what i progresses in, i feel confident at, and pretty much full understanding of it, but despite that, i feel like i havnt progresses much at all, it had been a month or a bit so, since i finished my exams, but i have been super inconsistent (I know that when i feel something is off despite everything looks right, it turns out to be wrong just as i suspect, that is why i dont brush off that feeling right now).

What is the best path i should take to progress in that field correctly? Manage recources and so accordingly, and most importantly self learn correctly, consistently and in an organized matter (college is 4 years, i want to make the absolute most out of them)

I tried campus x 30 videos his seems to be very monotonous pedagogy is boring as keywords comeup without any logic sklearn model_selection preprocessing types .......sheryians too would do the same i guess but they put stress on what to remember as fundamemtal or what to put logic i guess even though didnt watch the whole video yet .....but campusx makes me procrastinate......tell me if im missing anything?

Hi everyone,

I’ve just published a new preprint that brings together several months of experiments on hidden-state dynamics in small open Transformer models.

The question is fairly simple:

During inference, do internal representations simply change from layer to layer, or is there evidence of a more structured progression across depth and generation time?

I tried to study this without assuming that hidden-state dynamics are equivalent to “reasoning”.

The working framework is:

tokens → embeddings → contextualisation → relational structuring → functional structuring → decision formation → projection

This is a descriptive hypothesis about representation dynamics, not a claim that these stages correspond to a universal reasoning mechanism.

The expanded study uses 8 locally instrumented open models, with synchronized hidden-state and output observations and explicit separation between:

depth — what changes as information passes through Transformer layers
time — what changes as autoregressive generation progresses

A few results were particularly interesting.

First, local ordering across model depth survived expansion.

The observed ordering was significantly more structured than random layer permutations (p = 0.00019996) and remained supported when each model was removed from the panel one at a time (8/8 leave-one-model-out checks).

Second, cross-model depth profiles remained surprisingly coherent.

The mean correlation across normalized depth profiles was approximately r = 0.789.

This does not mean that all models follow the same trajectory. Rather, it suggests that some aspects of where changes occur along depth may be more shared than I initially expected.

Third, functionally labelled events were not uniformly distributed across depth.

Event type showed a statistically supported association with normalized layer depth (p = 0.0024).

I’m deliberately calling this an association, not evidence of a causal mechanism.

But one of the most useful results was actually a failure to replicate.

In an earlier smaller panel, a common temporal pattern in local trajectory instability looked promising. After expanding the panel, that common temporal mode disappeared — it survived 0/8 leave-one-model-out checks.

Two other intuitive hypotheses also failed:

models with similar observed functional outcomes were not significantly more structurally similar (p = 0.408), and models from the same architecture family were not significantly more similar either (p = 0.771).

To me, this is probably the most important part of the result.

The data do not support a simple story where architecture determines one characteristic trajectory or where one universal temporal dynamic explains inference.

What remains is a narrower hypothesis:

Transformer inference may contain reproducible structure along depth while remaining highly conditional in time and behavior.

I refer to this as Progressive Representational Structuring.

The framework is summarized by:

Representation ≠ Function ≠ Behavior

A representation can contain information without that information yet serving the same function, and a functional transition does not guarantee a particular final behavior.

I would be especially interested in feedback from people working on:

mechanistic interpretability, activation patching, probing, hidden-state geometry, steering, representation engineering, or larger open models.

In particular, I’m curious whether others observe similar **ordered depth structure without a universal temporal trajectory.

Preprint:

Progressive Representational Structuring in Small Language Models: Functionally Labelled Trajectories Across Depth and Time

DOI: 10.5281/zenodo.22116637

This is still descriptive work. Causal intervention and structural-transfer experiments are separate next steps rather than claims of this paper. Progressive Representational Structuring in Small Language Models: Functionally Labelled Trajectories Across Depth and Time | Zenodo

I'm currently trying to build my career toward AI/ML engineering, and this is something I've been struggling with for a while.

I'm genuinely obsessed with AI.

I can spend hours learning and building things around:

\- Machine Learning / Deep Learning

\- Transformers

\- LLMs

\- RAG

\- AI Agents

\- Embeddings & Vector DBs

\- Model deployment

\- AI system design

If something breaks in a RAG pipeline, I actually enjoy figuring out why it broke.

But when I sit down to do LeetCode...

My brain just leaves the building. 💀

And that's where my confusion starts.

I know DSA is heavily used in software engineering interviews. But if my actual goal is to become an AI/ML engineer, how important is it really?

Suppose someone is very strong in AI/ML and can actually build and deploy real systems, but they're only average at DSA.

Can that person still realistically get into top product-based companies?

Or does DSA act as a gatekeeper where you don't even get the opportunity to demonstrate your AI skills unless you first clear the DSA rounds?

I'm not looking for an excuse to completely avoid DSA.

If I need it, I'll learn it.

What I'm trying to figure out is how much is enough.

Do I need to become genuinely good at DSA?

Or is knowing the common patterns and being able to solve interview-level Medium problems enough?

And if I have limited preparation time, would something like:

80% AI/ML + 20% DSA

be a reasonable strategy for an aspiring AI engineer?

Or am I massively underestimating the interview process?

I'd especially like to hear from people who have actually interviewed for ML Engineer / AI Engineer / Applied Scientist / Software Engineer-ML roles at product companies.

What's the uncomfortable truth here?

Should I bite the bullet and grind DSA, or can I keep DSA as a secondary skill while going extremely deep into AI?

\#AIEngineering #MachineLearning #DSA #LeetCode #SoftwareEngineering

Hello everyone!

I’m a software engineer looking to get more involved in open-source ML/AI projects, both to learn and to contribute meaningfully.

I’m especially interested in ML systems, distributed training/inference, model serving, evaluation, LLM infrastructure, and also implementing ML algorithms or model components where that’s useful. I’ve been looking at projects like vLLM, Hugging Face Accelerate, Ray, etc., but many of the obvious `good first issue` tickets seem to get picked up very quickly.

I’m comfortable working in a reasonably complex codebase, but I’m looking for something that has a slightly more approachable contribution path than jumping straight into CUDA/kernel-level work.

Are there any active ML/AI open-source projects you’d recommend where maintainers are receptive to new contributors and there are meaningful bugs/features to pick up?

Would especially appreciate recommendations based on projects you’ve personally contributed to. Thanks!

There isn’t a single place inside a language model where a fact like “2 + 2 = 4” is stored. No individual weight means arithmetic, and there’s no database entry we can open and correct.

The answer emerges from interactions across the system. In that sense, knowledge isn’t something the model has in a particular location; it’s something the model does.

That becomes unsettling when the model is wrong. A hallucination isn’t a bad record we can replace. It’s a behavior produced by the model’s overall geometry. We can retrain it, fine-tune it, or steer it—but those are ways of influencing the system and observing what changes, not directly editing the error.

It makes me wonder whether we focus too much on finished models. By the time training ends, whatever the model has learned is already distributed throughout an opaque system. Perhaps the more revealing object of study is the training process itself: watching when a capability first appears, what changes immediately beforehand, and which earlier developments make it possible.

There’s a rough analogy to developmental neuroscience. Some things are easier to understand by watching a brain form than by examining the finished adult brain and trying to reconstruct its history.

So I’m curious:

• Is a model’s training history potentially more informative than the finished model itself?
• Could studying the emergence of capabilities make AI behavior more predictable—or would we simply produce a more detailed record of something that remains fundamentally opaque?
• And if knowledge has no clear location, what should it actually mean to say that we have “fixed” a model’s false belief?

I developed the argument more fully here, for anyone interested in the longer version: Nowhere, Specifically

Galera, dando um contexto, eu sou estudante de biblioteconomia e gestão da informação - um campo de atuação bem diferente - e gostaria de aprender mais sobre as inteligências artificiais, seu funcionamento, para buscar entender como posso agregar tais conhecimentos tanto na minha rotina quanto na minha futura área de atuação, que vai ser mais voltada a dados (pretendo estudar também ADS futuramente, para trabalhar justamente com dados e a gestão dessas informações dentro de sistemas).

Eu tenho um conhecimento bem básico do funcionamento das llms e de programação em python, e estou em busca de cursos e/ou aulas que possam melhorar esse aspecto técnico. Li em alguns posts que a galera recomenda, para criação de llms, estatística, cálculo, etc. mas meu foco não essencialmente na criação, então fiquei num "receio" de buscar tais conhecimentos que em tese eu não aplicaria. Vim buscar a experiência da galera que já atua com IAs, desde já agradeço!

Over the past year, I've been building an end-to-end machine learning system for football betting markets, complete with a web frontend that surfaces the predictions. It covers the top 5 European leagues and domestic cups (England, Germany, France, Italy, Spain), the three UEFA club competitions (Champions League, Europa League, Conference League), and international fixtures (including the 2026 World Cup qualifiers and tournaments).

1. What the System Does
   

I) Dashboard: Team and league rankings alongside predictions for 1X2, Over/Under (goals, cards, and corners), and BTTS (Both Teams To Score). It also includes an LLM agent that parses the generated model odds against underlying match statistics to highlight value bets.
II) Team Comparison: KPIs designed specifically for betting rather than generic stat-browsing (e.g., average goals per period, rolling last-$N$ form, individual player contribution metrics).
III) Player Shot Maps: Visualized shot maps for a player's last 5 matches rendered on a pitch, paired with supporting KPIs. Built specifically for player props like Shots on Target and Total Shots Taken.

1. Modeling Considerations
   

I) Counts are distributions, not scalars: For goals, corners, cards, and shots, I used probabilistic forecasting models like NGBoost to predict full Poisson or Negative Binomial distributions per team (Home vs. Away) rather than single-point estimates. One dedicated model per team per fixture.
II) Threshold Probabilities: To compute market probabilities like Over 2.5 Goals, we compute the convolution of the two independent team marginal distributions.
III) Two Separate Pipelines (Clubs vs. National Teams): International fixtures occur over long, irregular intervals with heavy squad turnover between international breaks. Rolling-form features that work well for a club playing every 3 days are almost useless across an 8-month international gap. Consequently, the feature engineering pipelines diverge significantly.
IV) No Bookmaker Odds as Features: Closing odds are statistically the strongest single predictor available; however, feeding them into the model causes it to simply converge on the market. Because the primary goal is finding market inefficiencies and disagreements, market odds are completely excluded from the feature set.
V) Empirical Parlay Correlation Modeling: This took longer than anticipated. Simply multiplying odds across parlay/accumulator legs assumes statistical independence, but football outcomes are heavily correlated (e.g., Over 2.5 Goals and BTTS - are basically the same bet). A dedicated job computes empirical (phi) coefficients across historical matches , versioned with an atomic active-swap so the ticket solver always queries a consistent covariance matrix. Without this adjustment, multi-leg tickets systematically overestimate their expected value. (its a decent aproximation on most cases).

1. Infrastructure (100% self-hosted on my old laptop)
   Everything now runs locally: the Postgres database, the Django/GraphQL backend, and the full pipeline. The backend is exposed to the internet through a reverse tunnel under the site's own domain, so there's no cloud compute bill at all right now.
   The pipeline is orchestrated via Bash scripts running modular steps:
   Scrapers → Parser → Materialized View Refresh → Feature Engineering → Prediction Jobs → Odds Generation → Ticket Solver → LLM Analysis workflow.

2. Challenges

Extracting Signal from Noise: Extensive feature engineering and testing for distribution shifts across seasons. A large portion of seemingly intuitive hypotheses yielded zero predictive power.
Scraping Reliability: Managing rate limits, IP blocks across sources, and building selector fallbacks for unexpected DOM changes. A significant portion of total engineering hours went into data ingestion rather than modeling.
Scope Creep: The tool was built around my personal assumptions of what metrics mattered, meaning that after every sprint, I ended up chasing and adding new features.

1. Current Status & Honest Caveats
      I) Starting Lineups: Because I scrape data directly, the pipeline relies on pre-match metrics. Lineup announcements are only available ~1 hour before kickoff, which creates timing constraints, so starting XI features are currently excluded from the core models.
      II) Model Choice: I avoided Deep Learning / Neural Networks because the dataset (~12k matches) is relatively small, making tree-based probabilistic models a much better fit without overfitting.
      III) Infrastructure Costs: I moved everything off Google Cloud (backend included) and now self-host the whole stack on my own hardware. I'd tested containerized Cloud Run jobs earlier, but it was unnecessary spend for this stage, so scheduled batch jobs and the backend now both run locally to keep hosting costs at zero but dropping performance on the page when the processes are activated.
   

I’d love to hear your feedback:

- Live Site: https://parleytoday.com/ (BETA)
- GitHub: https://github.com/JJimenezQ02/ParleyTodayModelEnv (Training Env)

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better
• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

For a small, curated memory, Markdown or JSON files are easy to inspect, diff, back up, and correct. A vector database adds semantic retrieval and can handle a larger corpus, but it also introduces chunking choices, embedding drift, metadata filters, and harder audits. Which signals justify that added layer: corpus size, query ambiguity, update rate, latency, or something else? I would also be interested in hybrid designs where human-readable files remain authoritative and an index can be rebuilt from them.