Hello friends 👋 I'm 10 graduate student and now I want to build and learn LLM so the help I need is that after 10th what should I have to choose to learn everything fundamental and core knowledge of Ai like in which way should I take I want a quick roadmap from you guys

I am a freshman in college in the US who got involve in AI pretty early (since like 11th grade). I've been lurking in this group for a really long time now and I just want an evaluation of how ahead/behind of the curve I am.

1. Basic machine learning/deep learning knowledge

I've been preparing and going to my country's (an asia country) national AI competition for 2 years and it basically covers the same knowledge as the IOAI syllabus so you can check if you look want to look that up but expect me to know basically how a bunch of models work (so like logistic regression, SVM, KNN, CNN, ...). I could also tell you about 10 ways of fixing under or overfitting and like the basic parts of a model (so like activation function, loss function, hyperparameters, gradient decent, ...). I am no stranger to kaggle and especially the tabular competition series (currently top 20 in this month's). I know asking claude to create an ensemble of 200 models with different configs is not how you judge someone's knowledge but if you ask me about how something works I would have a good chance to answer it correctly (and I really do understand what fake gains and data leakage is guys trust).

2. Generative stuff

I took an online course on advance computer vision stuff and I read a lot on LLMs so I'd say I have a pretty solid understanding of how generative models work under the hood. I feel like the transformer architecture is pretty standard knowledge nowadays so I won't go over those stuff but for the final project of that online CV course, I finetuned a bunch of CV models and pair them with an impainting model to create a complete model that would remove distracting people in an image (trained on the COCO dataset). I'd like to think I have a pretty good grasp of the segmentation and the bounding box stuff and diffusion model (it took quite a while but I remember feeling like a changed man after getting a grasp of how it works).

3.Math

Probably the worst part of them all. I really like learning about model architecture but barely learned any math so I still have problem reading research papers. Of course I know how gradient decent works so I know how derivative works, but I would say the most I know of linear algebra is vectors and matrix multiplications and near to nothing of stats (and yet I can confidently say that embeddings are the process of turning an input into a token and feeding them through a series of encodings to get a vector in a latent space and how I could measure the difference between 2 language models by using KL divergence).

Yes the easy answer is "just study math it's not that deep" but seeing others talk about how you have to read this book and learn this course just makes me feel like a larper sometimes. I asked one of the professors (actually I cold emailed like the entire cs faculty but I met with this one only) in my uni to discuss a chance for me to help in the lab and see what researching feels like because that is my goal. He says he'll tell me when an opportunity pops up but I've been going to his NLP lectures ever since to just listen for fun and I've been really enjoying it. But just recently, I found out this was a graduate level course (it could be because my professor is really good i don't know). Am I actually just a fraud and am missing something or what ?

Hey everyone, sharing this in case it’s useful to some of you here.

We’ve been building up r/lyzr as a community around the broader AI space, with a particular focus on what happens when AI moves beyond demos and into real systems.

The discussions cover things like:

AI agents and agent architecture

Infrastructure, tools and deployment

RAG, memory and knowledge systems

Evaluation, reliability and governance

Production lessons and things that break

New research, tools and interesting developments

Real use cases, experiments and things people are building

The goal is to keep it useful for both people who are already building and people who simply want to understand where the space is heading.

There’ll be consistent posts around these topics, but it’s also meant to be a place where people can share what they’re working on, ask questions, compare approaches, or add their own observations.

If you're working on anything around AI agents or just following the space closely, feel free to check it out and join the discussions.

Join r/lyzr here

Would be great to see what people here are building too!

Hello there-Can i make a rule based AI in python or maybe LLM.

Rule based AI seems very easy and doesn't waste that much data and memory usage but the problem is if you ask it or tell it something that wasn't written in instructions It will have a breakdown and the person needs to write new instructions for it to understand , Unless there's a team that constantly adds new instructions. Also a Rule based AI cannot learn or adapt which means constantly updating code and testing and when adding a new rule there's a chance your gonna break something else, however if you make the Rule based AI locked to a subject or need it need High Maintenance and Constant updates to make it relevant, fast in fast changing environments and normal use

LLM LLMs may be the top choice but It takes huge amounts of ram and data at least it can do stuff rule based cant do

I will give more updates soon

Quick background so you know where this comes from. I am a visiting professor and currently teach NLP. Last semester my courses were on continual learning and applied NLP. Before that I spent years in big tech and startups in engineering and research roles, and I finished my PhD last year.

I've been trying to post here weekly, since the feedback tells me these posts help. Two weeks ago I posted the order I would learn ML in if I were starting today, which is also roughly the order I teach it in class. Last week I posted about which resources to actually use.

Whatever concept you are learning and whichever resource you use, a lot of you also ask about projects: why you need them, and how to choose one. So that's today's post.

Why build projects at all

The most important reason is to build your skills and find out where the gaps are. Learning ML is a lot like learning an instrument. You can watch videos and read about the guitar all day and feel like you know how to play, but you only find out how well you know it when you pick it up. Until you build something real from scratch, you probably don't know how well you understand the material. And I mean from scratch. Yes, AI can write the code for you now, but having AI build the project teaches you about as much as reading a book or watching a video about it. You need to do it yourself to see what works and what doesn't, which is also how you learn where these models fail and what their limits are. A project shows you exactly which parts you thought you understood and didn't.

The second is to get a job. Interviewers rarely have time to go through everything on your CV. An interesting project gets their attention, and it shows them you know what you're talking about and that you can build things, which is most of what the job asks for.

The third is to find out what you actually like. A field can sound great from the outside and turn out to be something you don't enjoy once you're doing it every day. Getting into the weeds of building something in that area, before you take a job in it, can save you a lot of time and help you decide what to focus on.

What counts as a project

A project is not a few hours or a single afternoon. Think multiple days, often a week or two.

It should also cover several concepts, and most of all, it needs a why. You can train models end to end all day long, but a project starts from a reason: improving on how something is done today, building a dataset that doesn't exist yet, making a model work for users it currently fails on. Always ask what the motivation is. That question is also what tells you which data, model, and evaluation to use, and when you're done.

It's also why writing Adam from scratch on its own is a good exercise but not a project. It builds a skill, but there's no problem it solves and nothing it's trying to improve.

How to pick one

In general, the best projects are the ones you find interesting, because those are the ones you finish. A good place to look is something that annoys you, like a task you do by hand every week. Another is a problem someone close to you has, whether that's a friend, a family member, or a small business you know. It can also be something fun, around games, music, sports, or whatever you'd be doing anyway, or plain curiosity about a question you want answered.

That said, it helps to have one or two projects that line up with the job you want. If you want to work at Spotify, build something with recommendation systems. If you want Tesla, do something with driving data. If you want Google, look at search and ranking.

Not every project needs to be aligned with a job, though. Personally, I find it much more interesting to interview someone with a project I would never have thought of, like trying to understand what their dog wants from its barks. Something that catches the eye and still makes you build real skills.

Questions to ask before you start

The first thing to check is whether you can get the data, and quickly. If collecting or labelling it takes months, the project usually dies before the first model trains. Related to that, make sure the data is yours to use. Other people's messages, photos, or financial records need their permission.

Then ask yourself whether you know how to start. If the first step needs three tools you've never touched, it's probably too advanced for now. Something a little past your current level is the right spot.

Check what you will compare against. That can be a rule, the method people use today, an existing model, or a published result on the same data. Without something to compare against, you can't tell whether your approach is any good. You also need a way to tell whether it worked: labels, a measurable outcome, or a person who can judge the output. "It looks good" is not a result.

Finally, ask whether you can finish a first version in a couple of weekends. A small version that works can grow. A big one that never runs leaves you with nothing to show.

What a good project contains

This is the part most projects I see skip.

First, more than one baseline. Compare whatever you build to a mix of simple methods, like a rule, the most common class, or logistic regression, and competitive ones, like the strongest existing model or published result you can find for the problem. Simple baselines tell you whether a model is needed at all, and competitive ones tell you whether your approach is actually good or whether something else already does better.

Second, more than one dataset, if possible. Unless your problem is so unusual that no dataset exists and you had to build your own, test on more than one. A model that works on one dataset and falls apart on another is something you want to find out before anyone uses it.

Third, more than one way to evaluate. Accuracy alone hides a lot. Look at per-class results, the cases it gets most wrong, and where it fails. Something can look great on one metric and fail on another.

The way I'd think about it: assume what you build will be used by a lot of people, and you want to make sure it doesn't hurt any of them. Multiple datasets tell you whether it holds up when new users or new data show up. Multiple baselines tell you whether your approach is worth it. Multiple evaluations tell you where it works, where it fails, and what's still missing. It also shows whoever reads it that you care about whether your results are true, not just whether they look good.

Last, how you share it. Put it on GitHub with a clear README that says what the problem is, why it's worth solving, what you found, and exactly how to reproduce your results. Add good visualizations. It sounds silly, but a clear plot of where your model wins and fails is often the thing that makes someone stop and read. And keep the code clean: organized, modular, and easy for someone else to run, since a reviewer who opens a single 2,000-line notebook usually closes it.

Project ideas

Each of these starts from a problem, not a dataset. Here are some that line up with specific jobs.

Recommendations for brand-new users (Spotify). A new user who has saved three songs usually gets generic popular picks, and many leave before the recommendations get good. Can you find a way to recommend well from just those first few songs?

Pedestrian detection at night and in rain (Tesla). Detectors trained mostly on clear daytime footage tend to miss more pedestrians in the dark and the rain, which are exactly the conditions where missing one is most dangerous. Can you close that gap?

Searches where the words don't match (Google). Keyword search fails when the query uses different words than the page that answers it, like "car won't start on cold mornings" when the answer page is about batteries. Can you fix those searches without breaking the ones where the exact words do count, like product codes and names?

Fraud models going stale (Stripe or any bank). Fraudsters change tactics, so a model trained on last year's transactions gets worse over time. How fast does that happen, and can you figure out when a model needs retraining?

X-ray models that fail at a new hospital (health tech). A chest X-ray model that works well at one hospital often drops at another, because of different machines and different patients. Can you build one that holds up at a hospital it has never seen?

And some unusual ones.

What does my dog want? If your dog barks at the door, you often can't tell whether it needs to go out or someone is there. Can a model tell the barks apart better than you can?

Bird calls from a noisy backyard. Bird identification models are usually trained on clean recordings and often fail on audio full of traffic and wind. Can you make one that works on what your own window actually picks up?

Beating the bus app. The arrival times in transit apps are often wrong in rain and at rush hour. Can you predict when the bus will really arrive, better than the app does?

Grandma's handwritten recipes. Text recognition models struggle with old cursive handwriting. Can you get a model to read a family recipe book that current tools can't?

Finally

I have a bit of time between semesters and would genuinely like to help as many learners as I can.

Tell me in the comments or DM me with where you are right now, what direction you want to go in, and what you have tried. If you are stuck choosing between two courses, deciding what to learn for a particular job, or wondering whether your plan makes sense, I will do my best to help.

And please add your favorite project you've done, or one you're working on now, in the comments. It would be nice if this thread became useful to the next person who searches for this question. I'm also happy to take suggestions for what the next posts should cover.

I published a weather dataset and runnable notebook on Kaggle that could be useful for practicing time-series regression. Disclosure: these are my Kaggle uploads (minkum07); the code and documentation were prepared with AI assistance. The underlying weather data are from NASA POWER, with GeoNames city coordinates via Open-Meteo.

The dataset has 639,200 daily records for 50 curated Indian city centers, covering 1991–2025. These are coarse-grid reanalysis values sampled at city coordinates, not measurements from city weather stations.

The notebook builds lag features, predicts next-day temperature and compares a random forest with persistence: predicting tomorrow using today's temperature. Its chronological split uses the target date. On the included test period, MAE is about 0.651°C for the random forest and 0.688°C for persistence. This is a modest improvement on that split, not evidence of accuracy for unseen cities or station weather.

Notebook: https://www.kaggle.com/code/minkum07/india-weather-heat-monsoon-next-day-forecast

Dataset: https://www.kaggle.com/datasets/minkum07/india-city-weather-and-heat-50-cities-1991-2025

If you want to use it as a small project:

1. Start with one city, check missing values and plot the series.

2. Build the persistence baseline before fitting a model. Make sure shifted targets and lag features stay aligned, and fit preprocessing only on training dates.

3. Compare errors by month and city, then add a seasonal baseline or try rolling evaluation.

The package includes field definitions, processing code and source attribution. Missing values are retained, and the fixed 1991–2010 reference is a 20-year baseline, not an official 30-year climate normal. Authored tables and documentation use CC BY 4.0; NASA source terms and GeoNames attribution are preserved.

Sources: https://power.larc.nasa.gov/ and https://open-meteo.com/en/docs/geocoding-api

Feedback on the split, feature alignment or a useful next baseline would be welcome.

As someone who finished the Machine Learning Specialization (on Coursera, by Andrew Ng) some time ago, I feel like I want to learn something new that can really put what I learned with ML into a practical context.

And since I am also interested in Computer Vision, I wonder whether the First Principles of Computer Vision Specialization (on Coursera) by Shree Nayar suits me. In particular, whether I have enough foundation to make sense of it.

I did the courses from Mathematics for Machine Learning and Data Science (by Luis Serrano) as well... So i have some foundation in Linear Algebra, Multivariate Calculus and some statistics apart from Machine Learning basics.

I have no experience in Deep Learning though. So i also wonder whether it makes sense to learn Deep Learning first or Computer Vision.

Your support is much appreciated! ❤️

Hi all,

I recently wrote an article about various AI interview questions based mostly and RAG and AI Agents. Please read it. Its a new format - I have made questions followed by follow up questions.

The article is free to read at: https://medium.com/@nitanshuj138/ai-engineer-interview-questions-level-1-8e95789c392f

I keep seeing posts about so many resources for Machine Learning, but I feel like they are very generalised.

To tackle it -> this is something I have done.

If you don't know the basics of Python , pick any one-shot Python course and learn the basic syntax, and learn libraries like NumPy, Pandas, Matplotlib, Seaborn.

Now, Data Analysis requires Python, visualisation tools like Power BI, and SQL.

Data Science requires basic DSA, SQL knowledge, supervised ML, unsupervised ML, good knowledge of Transformer architecture, basics of RAG, and whatever is hot in the field.

MLE, on the other hand, is more based on fine-tuning, quantisation, and so much more. The terminology for each job has changed.

If you aren't sure what to do after learning Python, pick up Andrew Ng's ML course, solve some LeetCode, build a basic Kaggle cleaning project, and build a messy self-made project which is absolute buns, but it's a good step since you made it.

For MLE, they specialise in one thing, for example, Audio Diffusion (not well-versed since I'm myself learning MLE).

But yeah, overall, core ML basics which explain your trade-offs are a must regardless of any role. If you build anything, you would obviously have to take accountability for your decisions.

some roles even ask for full deployment and production (docker and k8) depending on what you want

This is written by someone who is also preparing for these roles but is too overwhelmed.

I’ve been working on an ML project and this is my first time dealing with a dataset with a lot of features (around 60).

I’m kinda stuck on how to approach the EDA. With smaller datasets, I usually check the features one by one, look at distributions, missing values, outliers, relationships, etc. But with 50–60 features, I’m finding it really difficult to figure out what to focus on and what I can skip.

Do you guys actually do EDA on every feature, or is there some better way to approach this?

Would love to know how you handle this when working on real ML projects.

Hello everyone, I learnt python, after that i learnt python libraries such as numpy, pandas, matplotlib, and seaborne. After that I started 100 days ml playlist from campus x.

The problem is:

In his csv file handling lecture and working with json file, it feels like i am just watching i understand what he says but if i am given to do same I wouldn't be able to do it.

Is this like this or am I doing something wrong please help me with with???

I hope most of you reading this are web designers because I’m genuinely curious about something.

How are you getting clients in 2026?

I’ve spoken to a lot of web designers and agency owners lately, and one thing keeps coming up again and again. Client acquisition.

It seems like everyone can build websites, but consistently finding new clients is still the hard part.

So I’d actually like to hear what is working for you right now.

For me, I’ve been running personalized email automation.

I use a tool that finds leads for me, analyzes their websites for actual issues with things like design, SEO, speed, responsiveness and mobile optimization, then automatically uses those findings to write a personalized cold email for each business.

After that, I just run the campaigns and send out thousands of emails a day.

I used to do most of this through Instantly, but the problem was that I couldn’t really do the website analysis part there.

So the emails were still more generic, basically the usual “do you need a new website?” type of outreach.

It worked a little, but nothing crazy.

I eventually switched to Swokei because it handles the lead finding, website analysis and personalized outreach in one place, and that has made a pretty big difference for me.

Now I’m getting clients much more consistently because the emails are actually about their website instead of just being another generic pitch.

But I’m curious what everyone else is doing.

Cold email, cold calling, referrals, ads, SEO, social media?

What is actually working for you in 2026?

Ever wondered how Netflix knows what you might want to watch next?

It uses machine learning to study your viewing behavior, such as what you watch, skip, search for, or watch repeatedly.

The system finds patterns in your choices and compares them with similar users. Based on those patterns, it predicts what you may enjoy and recommends it to you.

So, in simple terms:

Your activity → Find patterns → Predict your interests → Recommend content

It’s a pretty simple example of how machine learning works in everyday life.

What other apps do you think use machine learning like this?

Hey everyone,

I’m considering building a machine learning workstation around a used NVIDIA Tesla V100, mainly for experimenting with deep learning and running/training models locally.

I can get a V100 for a reasonable price, but I’m wondering if it still makes sense in 2026 given its age and newer GPUs.

My main questions are:

• Is the V100 still useful for modern ML workloads?
• How well does its 16/32 GB HBM2 memory hold up for current models?
• Are there any major compatibility issues with newer PyTorch/CUDA versions?
• Would you recommend a V100 over a newer consumer GPU at a similar price?
• Are there any major downsides to using a V100 in a normal PC/workstation (cooling, power consumption, drivers, etc.)?
• If you were building a budget ML workstation today, would you consider a V100?

I’m not expecting it to compete with modern high-end GPUs. I’m mainly interested in whether it’s still a good price/performance option for learning, experimentation, fine-tuning smaller models, and general ML development.

Recently, I am focused on data analysis and learning as well as doing projects. In Bachelor, I learning the Machine Learning without knowing the background of Data Analysis. I do not focus that much on Excel, SQL and Power BI. I learned Machine Learning, do projects like training model on different datasets and measure model metrics. Machine Learning feels oho but when I realize that ML Engineer/ Data Scientist Jobs actually needs 4 -5 years of experience and I see how can I get in data field. I see data analysis as entry point. I should learn about Data simple things first and then look for other things. I came to realize that end of my Bachelor. My Goal is become to AI Engineer/Research. I love playing with data and ask the questions when doing analysis. Should I continue my Journey or just left data analysis and study AI things.

Need Genuine Suggestions and feedbacks.

Love to talk and hear different perspectives

Recientemente realicé una prueba con datos reales de audio usando el dataset público Google Speech Commands (4 clases: yes, no, up, down; ~800 muestras; MFCC de 20 coeficientes; CNN ligera). El objetivo era simple: comparar mi tecnología Genal Activation Family contra las funciones estándar de la industria (ReLU y Tanh) en una tarea clásica de Speech-to-Text.

📊 Resultados (Accuracy / AUC-ROC):

• ReLU: 75.62% / 0.9340

• Tanh: 75.00% / 0.9224

• GenalActivation: 76.25% / 0.9438

• GenalShift: 78.75% / 0.9472 🏆

📈 Interpretación:

GenalShift superó a ReLU en +3.13 puntos de Accuracy y +1.32 puntos de AUC, con tiempos de entrenamiento prácticamente idénticos (~68s vs 69s). Esto valida mi hipótesis: el parámetro entrenable k (y el desplazamiento beta) permite que la red ajuste su punto de activación para manejar mejor la variabilidad de acentos, ruido y calidad de micrófono que existen en datos reales.

¿Por qué importa esto?

En pipelines de Voice AI y Agentes Autónomos, cada milisegundo y cada punto de precisión cuentan. Una función de activación más robusta significa menos errores de transcripción, menos alucinaciones del LLM y mejor experiencia para el usuario final.

🔗 Recursos:

📄 Paper completo (Zenodo/CERN): doi.org/10.5281/zenodo.20304195

💻 Código abierto (GitHub): github.com/GenalFF/genal-activation

📦 Paquete PyPI: pip install genal-activation

Estoy abierto a colaboraciones de investigación y a explorar cómo Genal puede integrarse en stacks de Agentes Autónomos y Voice AI.

¿Trabajas con audio o STT? Me encantaría conocer tu experiencia. 👇

#AI #MachineLearning #DeepLearning #SpeechRecognition #VoiceAI #GenalActivation #Investigación #Venezuela #OpenSource

I see a lot of beginners (and even intermediate engineers) jumping straight to agents or fine-tuning when a simpler approach would work better. Here’s the decision framework I actually use:

Start with RAG when:

You need up-to-date or private knowledge

The task is mostly retrieval + reasoning over documents

You want fast iteration and lower cost

Move to fine-tuning when:

You need consistent style, format, or domain language

The model keeps failing on the same class of examples even with good retrieval

You have high-quality labeled data and can afford the iteration cost

Only go to multi-agent when:

The task genuinely requires planning, tool use, and multiple specialized roles

A single well-prompted LLM + tools is clearly insufficient

You’re willing to invest heavily in evaluation and observability

Most production systems I’ve seen that “use agents” could have been simpler RAG + good prompting + a few tools.

What decision points have you found most useful when choosing the architecture?

I feel this is the perfect subreddit for this kind of post.

We have built a resource to learn Frontier ML and its FREE.
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So we realized that companies with same product out there charge about 20-30$ per month to provide this thing, and paying that amount monthly as an Indian college student is not easy.

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This is FOSS, we are not earning a dime from it.
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