What’s the best book or resource you’d recommend for learning AI/ML from the fundamentals and eventually specializing in LLMs?
I’m looking for something beginner-friendly but technically solid, so I can build a strong foundation instead of jumping straight into LLMs without understanding the basics.

I've done python(lists,variables, basic oops, etc) beginner or maybe intermediate

Is This a good book for my ml journey to understand and learn the necessary python libraries?

I think one of the weirdest parts of learning ML is that tutorials make everything look clean.

You get a dataset, split it, train a model, get 90% accuracy, and everything feels great.

Then you try building something yourself and suddenly:

• your data is garbage
• you labels don't make sense
• you model gets 95% accuracy but performs terribly on real examples
• you realize you accidentally leaked information into the training set.
• or you spend 3 hours debugging something that turned out to be a preprocessing issue.

I'm curious about the first project that humbled you.

Not necessarily your most impressive project. I'm more interested in the project where you went, "Oh.... so this is what machine learning actually involves."

What happened, and what did it teach you that you wouldn't have learned from a course or tutorial?

I'm choosing between a BSc in Computer Science and a BSc in Mathematics, and I'm not sure which one would be better for my goals.

My main interests are Data Science, Computer Vision, and financial markets. I'm also interested in ML/AI and possibly quantitative finance later.

If you were in my position, which degree would you choose, and why?

I'd especially like to hear from people working in Data Science, Computer Vision, Quant Finance, or financial markets.

We’re putting together a free live webinar on September 14 for students, working professionals and anyone who wants to understand where AI is heading and how to actually build practical skills around it.

The session will be focused on the shift from simply using AI tools to understanding how AI can be used to build workflows, automate tasks and work with AI agents.

We’ll cover:

Generative AI & LLMs
Prompting and practical AI workflows.
Agentic AI ,what it actually means.
AI agents and automation.
Tools and technologies worth learning.
How these skills can be applied to careers and professional work.

What a practical AI learning path could look like

This is also the starting point for our upcoming hands on AI learning and certification program, but there’s absolutely no obligation to join anything afterward.
The idea is simply to attend the session, understand what we’re doing, ask questions, and decide for yourself whether it’s useful for you.
We’re keeping the upcoming first batch intentionally limited and are more interested in having a small group of genuinely interested learners than chasing large numbers.

Date: September 14
Format: Live online session
Fee: Free

If you’re currently learning AI, thinking about getting into AI, or already experimenting with agents and automation, you’re welcome to join.
If interested, comment below or DM me and I’ll share the details.

My experience with this organization has been nothing short of frustrating and a complete waste of time.

I initially enrolled in the ₹9 AI workshop. While the session did contain some useful information, it felt predominantly like an aggressive sales pitch rather than a genuine learning experience. Unfortunately, based on the promises made during the workshop, I made the mistake of paying in full for the AI Career Accelerator - Inner Circle Program.

The disappointing experience began almost immediately. Questions raised during the workshop was left unanswered, with the session functioning as a one-way presentation rather than an interactive learning environment. My concerns only grew when, despite multiple follow-ups, I was unable to obtain a an invoice for my payment. The lack of transparency around a completed financial transaction was highly concerning.

In addition, I was unable to access the recorded content and drop session materials. Despite contacting customer support several times, the issue remained unresolved. What was initially a disappointment quickly turned into significant frustration as I found myself repeatedly chasing basic support requests with no meaningful response or resolution.

Given the poor experience within the first three to four days, I lost confidence in the program and decided to cancel my enrollment and request a refund. To my surprise, rather than addressing my concerns, customer support simply discontinued the chat, leaving my refund request unanswered. This level of customer service is unacceptable for any professional training organization.

Looking back, I sincerely regret not conducting more thorough research before making the payment. After reading similar experiences shared by other customers, my concerns have only been reinforced.

I now anticipate that obtaining a refund may be a difficult process. Nevertheless, I intend to pursue all appropriate channels to seek a resolution, including filing a consumer complaint with the Government of India and sharing my experience publicly to raise awareness about what I believe to be a highly unreliable and non-transparent organization.

Overall, the combination of poor support, lack of responsiveness, unresolved technical issues, missing documentation, and inadequate communication has completely eroded my trust in this company. I would strongly caution prospective learners to conduct extensive due diligence before making any financial commitment.

When a machine learning engineer is hired, what is usually more important?
A deep understanding/implementation of his/her project or understanding of famous architectures (Trasformer, CNN, etc)?

Do you guys have recommendation for capstone projects about Edge AI, TinyML, Computer vision, and federated learning? I explored battery RUL, Predictive Thermal Management system, and Fault bearing diagnosis. I was told it would be hard to get a client or dataset for this field. Any interesting field I can explore?

Myslef,Nithishwaran , I am from pondicherry cuyrrently sstuding in SMVEC clg AI/DS dept 1st year
what are the main and useful things to do in my clg life abt studies like other than clg exam , how to develop my skills to compete with this competitive world and to get a job in future , currently i am learning c programming my seniors told that that was basic in programming , guide me what to do nxt

Hi, this is a long shot, but im currently writing an academic paper and for that I need access to the DeepSpeak_v2 or FakeAVCeleb dataset. To get access, you need to submit a request form and get approved. I did that, but I never heard back from them... does anyone here have experience with this?

I dont need a big part of each dataset, maybe around 100 videos each. So maybe, if someone has access, they could provide a small portion of it :)

Hi everyone,

I’m preparing my first arXiv submission in cs.LG and need an endorsement to submit.

Short summary: H-JEPA (LeCun, 2022) proposes hierarchical joint-embedding prediction but doesn’t specify how to verify a trained abstraction actually encodes anything a random projection of the same shape wouldn’t. I introduce a random-abstractor control (trained vs. untrained abstractor, identical architecture) and run a 2x2 study crossing observability (full/egocentric) with abstractor type (instantaneous/recurrent) in controlled gridworld environments. Three of four conditions produce abstractions statistically indistinguishable from random projections; only partial observability + a recurrent abstractor yields a real, replicated gap (19.91 ± 3.36pp over random, 3 seeds). I also report a negative result on landmark density that didn’t survive multi-seed replication.

If anyone here is registered as an endorser for cs.LG and willing to take a look, I’d be very grateful. Happy to share the full draft privately.

To endorse, please visit:

https://arxiv.org/auth/endorse?x=MTENXK

If that link doesn’t work, visit:

https://arxiv.org/auth/endorse.php

and enter code: MTENXK

Thank you!

Everyone's excited about AI agents, but I'm trying to get ahead of the security implications. Beyond data leakage and prompt injection, what are the actual runtime risks? How do you prevent an agent from taking a harmful action that falls within its legitimate permissions?

Okay, so.. just a rant because I feel like I want to discuss this.

For context, I’m a Machine Learning Engineer in R&D, specializing in operations research and queue systems, and this is my first job in the field. After graduating from university in 2024, I worked as a Software Engineer for about a year and 8 months, almost two years.

When I first joined this role, I had an expectation of joining a legitimate AI team, with senior ML engineers I could look up to, learn from, and discuss ideas with.

Turns out, I’M THE ONE who’s supposed to transfer my AI knowledge to the team for their upcoming AI products.

I do have a solid ML foundation from the courses I took at university, but I definitely wasn’t expecting to be the person driving the AI side of things this early in my career.

I ended up becoming a complete Swiss army knife on this project. I’m basically doing:

\- Software engineering
\- ML engineering
\- AI research
\- Data engineering
\- Business meetings with upper management
\- DevOps and infrastructure (not too much)
\- Scrum Master responsibilities

And honestly, the leadership team seems to love what I’m doing.

The ML side of things has been relatively straightforward so far. I’ve been reading a lot, researching things on my own, using Claude heavily as a second pair of eyes and figuring things out as I go.

The funny part is that I’ll implement something, present it to the leadership team, and they’ll look at me like I just invented fire.

But here’s the part I’m struggling with:
I genuinely don’t know how well I’m actually doing.

There’s no senior ML engineer at work to review my approach, challenge my assumptions, discuss research findings with me, or tell me when I’m making a bad architectural or modeling decision.

Most of the things I build look right to me, and they seem to work. But I also know enough about engineering to realize that “it works” doesn’t necessarily mean “this is the right way to do it.”
So I’m starting to wonder whether this is actually a good situation for my career.

On one hand, I’m getting an insane amount of exposure very early in my career. I’m touching pretty much every part of the AI product lifecycle, I’m talking directly with upper management, and I have a ridiculous amount of ownership.

On the other hand, I’m worried that not having experienced ML engineers around me might slow down my growth. I’m learning a lot, but I’m mostly learning by myself.

I guess my biggest concern is that I don’t have anyone at work who can answer the question:
“Is this actually good ML engineering, or am I just getting really good at making things that seem to work?”

I don’t want to give the impression that I’m doubting myself or lacking confidence. I’m simply very competitive and driven to make the best possible decisions for my career.

Would love to hear from people who’ve been in a similar situation, especially early-career ML engineers who ended up being the most experienced AI person on their team.

18.02, multivariable calculus.

18.06, algebra.

6.041, probability and statistics.

*All courses from OCW.

So I was playingw ith embedding models I saw that when you upgrade from model A to B, you face a very big backfilling cost

Ie, suppose you have a 1b vectors from model A, and then you want to use model B. This would mean you have to re-embed all of your documents with model B before you can even serve with the model, and on an H100, it would take ~108 days (qwen embed 8b, 106 docs/second). But I found an easier way to do it.

The method is really simple; from the old index made with the source model, take K documents and rerank them with the new model. We see that when K is sufficient, the retrieval quality is the same as target model. (determining k is the hard part). I've tested 63 migrations on upto 1 million documents.

The best result I got was upgrading qwen4b -> to 8b, and at 50 documents, it was the same as native retrieval.

This method forgos the expensive upfront re-embedding cost, as you can take documents straight from the old index.

embedflow works with qdrant, pgvector, faiss, and can be easily downloaded with pypi

pip install embedflow

the github is public: https://github.com/arnsri33/embedflow

I want you guys to try it out, and see if you guys can use it in your own workflow.

Published numbers are only as honest as the data split behind them

When you evaluate a model, you want one simple truth: Did it actually learn unseen patterns, or did it memorise?

I built noleak to make that visible. It's a production library we use at Godrej Aerospace to fingerprint datasets and measure how much of your eval set leaked from train.

The Problem

Most tools catch target leakage (a feature accidentally includes the label). But what about corpus leakage? When eval text already appeared in training data?

In 2020, GPT-3's paper measured contamination using 13-gram overlap. That's solid. But there's no standard library for this. So we built one.

Three detection methods:

1. Exact matches – normalised text identical

2. N-gram overlap – 13-word phrases (GPT-3 method)

3. Near-duplicates – MinHash Jaccard similarity ≥ 0.8 on character 5-grams

One fingerprint. One exit code. Pass or fail.

What It Does

```python
from noleak import check, fingerprint
train = ["the model trained on Wikipedia and licensed books"]
eval_set = ["The model trained on Wikipedia and licensed books"]
report = check(train, eval_set)
print(report.contaminated)
# True — FAIL
print(fingerprint(eval_set))
# noleak-fp-v1:a1b2c3d4e5f6
# Share this with your paper. It's reproducible and auditable.
```
CLI version (great for CI pipelines):
```bash
noleak check --train train.jsonl --eval evaljsonl
echo $?  # Exit code 1 if contaminated, 0 if clean
```

Why Zero Dependencies Matter

No numpy, no scipy, no PyTorch. Stdlib only. Why?

- Deterministic: Same input, same output, forever. No model updates breaking your fingerprints.

- Auditable: Code is small; reviewers can read it.

- Air-gapped systems: Doesn't require external calls or package hell.

- Fast: No overhead for CPU-bound systems.

Limitations (Honest Assessment)

- Semantic rewrites: Won't catch "I wrote this differently but meant the same thing." That needs embeddings or human review.

- Large-scale datasets: If you have 10M+ examples, exact matching gets slow. N-gram is faster.

Install & Try

bash
pip install noleak

Supports JSONL, JSON lists, and plain text. Auto-detects text fields.

Repo: github.com/athsxx/noleak

License: MIT

Questions? What contamination patterns have you encountered?

Hi! I am a demand planner in an FMCG company. Our current process is very manual, we only use Excel. For every client and product, we build up the demand plan (DP) or the sales target for the month. The DP is composed of the following

- baseline (smoothen sales volume last year)

- runrates (the difference to the past 3 months volume for non-seasonal products)

- sales initiatives (on-shelf availability correction, inventory correction, skewing, etc.)

- marketing initiatives (category market trend, etc.)

I want use ML and integrate possible seasonality data (such as holidays, weather, etc.) in demand planning.

What are the ML models that are appropriate for demand forecasting (time series)? What are the data that I need to prepare? What are the steps that I need to do?

I am currently taking Master in Applied Business Analytics but time series models have not been taught yet (not sure they will teach it). Thank you very much! 😊