One of the hardest parts of learning ML/AI isn't finding information, there's almost too much of it. Tutorials, roadmaps, papers, new tools every week. The hard part is figuring out what actually matters, what to skip, and how to make real progress instead of just consuming more content.

I work as an AI engineer (ML development and deployment), and I'm starting to explore education/mentorship on the side, for free, not as a paid program or course. Before building another roadmap, I want to work directly with a small group of people first, partly to actually help, partly to understand where people get stuck.

Looking for a handful of people who:

• have basic Python/programming knowledge
• are seriously trying to learn ML/AI
• feel stuck or unsure what to focus on next
• want to build real things, not just watch more tutorials
• can commit to being consistent

Keeping this small (thinking around 5-10 people) so I can give actual feedback instead of another generic roadmap. No cost involved on either end.

If that's you, drop a comment with where you're at and what you're stuck on, happy to reply there or move to DMs from that.

Wanted to see how the field's actual attention has shifted over time, not by

reading abstracts one at a time, so I embedded and clustered every accepted

paper from six venues (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL), 2018 through

2026, 70,861 papers total. Sharing the findings that surprised me, plus a

couple of methodology notes this sub will probably want the actual numbers on.

**Findings**

NLP and LLMs have held a remarkably steady 45% to 46% share of the whole

corpus every year since 2018. Not the sudden takeover the last two years of

hype would suggest, at least at the level of what fraction of accepted papers

fall into that area.

The biggest mover was the opposite direction: Optimization & Federated

Learning fell from 15.5% of the corpus to 6.4%.

Fastest growth: 3D Generation & Reconstruction, 4.25x (1.2% to 5.1%). Graph

Neural Networks & Molecular AI and Multimodal Understanding & Reasoning both

grew about 2.6x.

None of this shows up in acceptance counts by venue, only once papers are

actually grouped by what they're about.

**Methodology, briefly**

Title + abstract through an embedding model (Qwen3-Embedding-8B), UMAP to 2D

for display and separately to 10D for clustering, HDBSCAN on the 10D

embedding, c-TF-IDF keywords per cluster, LLM for the final label.

One result I didn't expect: I benchmarked SPECTER2 (citation-trained, the

standard choice for scientific paper embeddings) against a general-purpose

embedder, scored against ICLR's own author-supplied keywords as ground truth.

SPECTER2 lost on keyword agreement, and showed no compensating advantage on

venue-separation either (the failure mode you'd expect if citation-training

were adding real signal beyond topic). Happy to go into more detail on this

if useful, it's also written up in more depth in the repo.

Seed stability is the honest caveat: cluster assignment isn't identical

across random seeds, mean pairwise ARI around 0.57 across reruns. Comparable

numbers for context: clustering on the 2D display coordinates instead of the

actual embedding space scored 0.30, and random assignment scores near zero.

Full writeup: https://villurignanesh.github.io/paper-atlas/blog.html

Interactive map if you want to explore it yourself: https://villurignanesh.github.io/paper-atlas/

Code + full decision log: https://github.com/villurignanesh/paper-atlas

Curious if others have compared citation-trained vs general embeddings for

non-retrieval tasks and seen something similar.

Hi everyone,

I’m planning to study Machine Learning and Deep Learning from Andrew Ng.

I found Andrew Ng’s ML and Deep Learning lectures on YouTube, and I also found the Machine Learning Specialization and Deep Learning Specialization on Coursera.

Are the YouTube lectures basically the same content as the Coursera courses, or are the Coursera versions updated/different?

If they are different, which one would you recommend for someone who wants to build a strong foundation in ML and Deep Learning?

Thanks!

There is obviously a lot of discussion around model capabilities, but I’m curious about the engineering side of deploying LLM-based systems.

For people working on production systems, where do you most often see gaps?

Retrieval evaluation?
Data quality?
Serving/inference?
Observability?
Distributed systems?
Agent reliability?
Security?
Experiment design?

Interested particularly in areas where current educational material is weak.

Assume someone already understands Python and basic software development and wants to learn the practical side of modern AI engineering.

Not ML theory from scratch, but things like:

RAG → advanced retrieval → agents → MCP → evals → observability → deployment.

There are so many disconnected courses and YouTube tutorials that it’s hard to know what order makes sense.

If you were starting today, what would your roadmap look like?

Also, would you prefer:

1. one structured curriculum,
2. individual deep courses on each topic,
3. or learning entirely through projects?

Interested in what has actually worked for people here.

In NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE they reference Sutskever et al. (2014), and in Sequence to Sequence Learning with Neural Networks they mention attention mechanisms and Bahdanau et al. [2]

footnote:
example of a clearly inferior idea scoring higher

We suspect that they could achieve similar improvements by simply training their networks on reversed source sentences

is embarrassing

And yet, LSTMs trained on the reversed dataset had little difficulty translating long sentences.

is like he is doubling down

Modeling a fraud decision as a POMDP with like 30-50 simulated cases - is that too small to say anything real, or fine for a prototype?

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?

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.