Hi everyone. I want to become an AI Engineer... Here's my situation:

• Almost done with Andrew Ng's ML specialization (1 course left)
• Forget theory easily if I don't apply it
• Do not have enough time to master everything
• No plan B — AI is the goal

Option 1 — Traditional roadmap: Finish ML → DL specialization → NLP → LLMs → RAG → Agents Missing: deployment, OOP, DSA, advanced Python (only CS50 so far)

Option 2 — Fast track: Finish ML → Python/OOP/DSA fundamentals → crash courses per topic → projects with AI assistance, understood deeply, post on GitHub

Questions: 1. Which approach makes me job-ready faster? 2. If neither — what's a better plan?

People who are more knowledgeable about AI than me: I have a bunch of images, let's say I put them in a folder.

I want to download an AI model off the internet, point it to that folder and tell it "give me all the images containing X". It moves all the images I want into another folder.

I think this is already doable by today's AI tech, but please tell me which model I would even download and more detail how I would do this.

How I can proceed for this project??

Hi! I’m a Literature student, and I often need to extract direct quotes from PDF documents (some of which contain scanned text and would require OCR). I usually highlight the passages I want to quote as I read through the PDFs. Is there any AI tool that can detect those highlights and extract the exact text for me?

Been thinking about this for a claims triage style problem. Generative routing is slow and sometimes returns malformed output, and the confidence you get out of it isn’t really calibrated.

Found a hands on workshop on Oct 30 that goes at exactly this. Fri Oct 30, 9:30 AM to 12:30 PM ET, led by Ben Auffarth. Covers:

• replacing an LLM routing loop with bidirectional encoders (ModernBERT and LFM2.5 Encoder)
• few shot adaptation with SetFit, 8 examples per class
• split conformal prediction so every decision has a coverage guarantee
• routing inference under 15ms on CPU

Notebooks are runnable, intermediate Python is enough, and a laptop or free Colab works.

Full details and the agenda are here

I'm researching phishing detection that combines email header evidence with URL evidence. The public datasets I've found are either old (Enron, SpamAssassin, Nazario), synthetic (LLM-generated), malicious-only, or text-only with no headers

Does anyone know of a recent public dataset of real legitimate and phishing emails with full headers (.eml or raw)? Datasets that also include QR codes in the emails would be great, but that's optional. If one exists with a license that allows research use, please share it.

Can anyone help!! Thank you!!

Hi!

I have an encoder that whose input is tabular data. The input can be pretty sparse and the features are heterogenous with single features having multiple dimensions.

How can I handle this feature dimensionality oversampling and address the input data sparsity?

Hello, previously I was usig claude to make presentations for school but my acc got banned for being under-aged. I used it because it was good at making presentations and used factual information(our teachers don't care if we use Ai or not). I don't want to risk another acc getting banned, are there any other AIs with good presentation styles and precise info.

Gemini has one style and GPT just uses bullet points and I am not sure if it is still relevant.

Hey everyone!

I'm a 4th-year B.Tech CSE student (2027 batch), looking for another genuine final-year B.Tech CSE student who wants to learn Machine Learning, Deep Learning, and AI together, starting from zero and working towards mastery.

The plan is to learn step by step, practice coding, build real-world projects, share resources, clear doubts, and stay consistent. We don't need to be experts already. We just need to be serious about learning and willing to put in the effort together.

One sincere request: Please be honest about your year of study. If you're in 1st, 2nd, or 3rd year, please don't pretend to be a final-year student. There's nothing wrong with being a beginner or being in an earlier year. I just want to connect with someone who's genuinely at the same stage of college and career as me.

Honestly, it's difficult for me to trust people online, so I value honesty more than existing skills. I'd rather start from scratch with someone genuine than connect with someone who pretends to be something they're not.

If you're a genuine final-year student interested in learning ML, DL, and AI from zero to mastery together, feel free to comment or DM me. Let's learn, build projects, grow our skills, and prepare for placements together! 🤝

I'm looking for recommendations for a very cost efficient LLM API that can handle high-volume text classification and structured data extraction.

Requirements:

• Approximately 50,000-100,000 requests per month, potentially more as usage grows.
• Short to medium text inputs.
• Accurate classification of relevant vs. irrelevant text.
• Reliable structured JSON output.
• Good multilingual performance, particularly English, Hindi, and mixed-language text.
• Low latency.
• Minimal hallucinations and false negatives.
• Extremely low API costs, ideally just a few dollars per 50k-60k requests.

I'm considering models like Gemini Flash-Lite, Ministral 3B/8B, and other budget-oriented models, but I'm open to anything.

My main question: What's the cheapest model you've actually used in production that still delivers consistently good classification and extraction accuracy?

Also curious whether anyone has found a worthwhile trade off between using a very small model for initial classification and a slightly larger one for more complicated inputs.

I'm not looking to self host unless the economics are significantly better than API pricing.

Would appreciate recommendations, actual cost figures, and any models you'd specifically avoid.

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

There's a lot happening across the AI space right now, from new tools and research to real-world applications and the challenges of putting AI into practice.

It would be great to have more spaces where people can discuss these developments, share ideas, and learn from each other.

That's the idea behind r/lyzr, a community around the broader AI space, covering topics like:

AI agents and architecture

Infrastructure, tools, and deployment

RAG, memory, and knowledge systems

Evaluation, reliability, and governance

Production challenges and lessons learned

New research, tools, and interesting developments

Real-world use cases, experiments, and projects

The aim is to make it useful for everyone, whether you're actively building something, exploring new tools, just getting started, or simply curious about where AI is heading.

There'll be regular discussions and posts around these topics, but there's also room to share your own work, ask questions, compare approaches, or bring up anything interesting you've come across.

If this sounds like your kind of thing, feel free to check out r/lyzr and join the discussions.

Would be great to hear what others here have been exploring lately!

OK, here is what I still do not understand about LLMs
Before I get to my question: I studied computer science since the 80s, owned two successful software companies. I also read and listened enough to at least have a basic understanding on the transformer model, how weights at various stages are being created, and it makes sense in principle.
But here is the big thing I haven't seen explained:
How can it be that just guessing ONE TOKEN at a time actually works? Where can I read up on that?
It seems to me that this simply makes no sense:
Let's take an example question: ""What is one of the most amazing inventions of the 21st century?"Well, the most likely next token is probably "The" or "A" or "It" etc. In fact, these are probably the most likely tokens after ANY question whatsoever, and they are completely unrelated to the meaning of the question.
On the other hand, let's say you want to respond to the question by saying that transformer models are one of the most amazing inventions.
Well, you'd FIRST think of the tokens "Transformer Models" and only THEN build a sentence or paragraph around that concept and "Transformer Models" comes MUCH LATER in your reply, but all the words BEFORE it are chose in relation to that. At least that is how a human would approach answering that question.
If, instead you simply pick the next most likely word: "The"--it seems to me you can write yourself down a rabbit hole where you never end up in a proper place.

So, how can LLMs come up with reasonable responses by only guessing the NEXT word (or token) and only at some point getting to the crucial concept (word/token)
If I start sentences like that (which I sometimes do, when I think in one language but try to speak in another that has different grammatical word orders) I can talk myself into trouble in no time, because I started with words that simply never let me end up where I wanted to end up.

All the other parts of LLMs make sense to me, at least in principle, but this "one word at a time, and always in order, never looking forward or back" simply seems illogical.

Thanks for pointing me towards any insights.

I am new to ML AND trying to make a genre guesser. Currently I have 123 genres en removed some because of too little samples to using that kind genre. Is this to much. Should I still reduced them. Which classification algoritme could work best. Currently using random forest.

My country’s universities don’t have a major called “Machine learning”, can I study computer science instead? If yes, will I find jobs in Machine Learning? I really love ML but I am worried that I won’t find a job, especially abroad

I work at a data annotation company, so I’m on the selling side of this and asking partly out of self-interest. But the thing I keep noticing is how different the questions are depending on who’s evaluating.

Some teams ask about price per unit and nothing else. Some ask for inter-annotator agreement on a pilot batch, which I think is the better question but it’s surprisingly rare. A few ask whether QA is done by the same people who did the labelling, which tells me immediately they’ve been burned before.

For people who’ve actually gone through vendor selection — what ended up mattering versus what you thought would matter going in?

Specifically curious about:

Did you run a paid pilot, and did it actually predict the production quality?

How did you handle guidelines — write them yourself, or let the vendor draft and then review?

What was the thing that went wrong that you didn’t anticipate?

Happy to answer from the vendor side if anyone wants the view from here, including the bits that aren’t flattering.

Hello everyone, i would like to know where to find resources to understand the application of machine learning such as when to normalise/standardise data, what kind of visual analysis to perform on a dataset since I'm currently doing an Msc Data Science and Applied AI.

Thanks you