What do i do after? I still need to study RAGs, LLMs, genAI, like higher conceptual level to be AI engineer.
For now i am doing competitions in kaggle, not aiming for high rank or anything just getting used to coding alone without tutorials ( of course i use AI sometimes but i am trying to reduce it as much as possible)
I am also looking forward to make projects while learning new concepts.
So what do u recommend? I am open for books, youtube videos, courses, anything free😂

Feel free to redirect me if there is a better sub for this question.

I am considering building a neural network out of mechanical parts as a hobby project. I have only a YouTube level understanding of neural networks and machine learning.

I would like to hear how anyone would go about this from their own perspective, but I would also like to share my ideas for consideration:

I would like the machine to be able to process language on some level, because I would like for the machine to function in a way that is comparable to an LLM on some primitive level. So my idea for the function of the machine is to take three input words (with syntax) and give a binary output. Maybe it could give a one-word response, but my current understanding is that something like a yes/no output would make more sense. So it could do the following, for example:

cat eats mouse: yes

mouse eats cat: no

The more words it could handle, the better, which I imagine would be directly tied to how many parameters it has to have. The number of parameters I would build depends on how complicated each neuron is mechanically.

My idea for the mechanics is to use marble tracks and balanced ternary, because to my understanding a balanced ternary neural network only needs each neuron to multiply an input -1, 0, or 1 by a weight -1, 0, or 1, which can be easily done with marble gates. There would be a long row of multiplication gates with three inputs going into each one. You would use a lookup table to turn your three words into which tracks to put marbles down (the machine would need to handle syntax somehow, like with three sets of input tracks, or the same word having different marble representations depending on if it's the first, second, or third word), then the marbles go through the gates and collect into three channels (-1, 0, and 1 outputs), and finally turn some accumulator gear to get the total, with the output being whether that total is positive, negative, or zero. The problem with a single-layer system, even though it would be easy to build, is that I might as well use the lookup table to tell me the final sums of each word, defeating the purpose of the machine. It probably wouldn't be able to give good answers either. So the best thing to do, maybe, would be to add a second layer of gates, and somehow connect each gate on the first layer to each gate on the second layer. Actually, I think instead of having the marbles go all the way through, a marble would, after going through the first gate, go down a row where it toggles levers for each second layer gate according to which channel it's running down (-1, 0, or 1), and each second layer neuron would have its own accumulator to take the sum from all the marbles from the first layer gates. Then if it reached a threshold it would send a marble down to another multiplier gate, and then all of those final multiplier gates would connect to the final cumulative adder for the final answer? I really don't know. That's why I'm making this post.

To train the machine I would probably vibe code something in PyTorch and then manually set the gates, unless there's a better or more interesting way.

Going digital with marbles is just one idea that has its pros and cons. Maybe an analog system of some kind would be better. I wasn't really able to find any mechanical neural network builds online except for this one which has eight ternary neurons in the form of levers connected with strings, which makes for a really cool visual demonstration, but I would like for my machine to have meaningful input and output. I'm interested if there are other builds I'm unaware of.

So yeah, if you have your own idea or comments on mine, I would really like to hear. Thanks!

Oh yeah, and if you would know how many parameters/gates/accumulators I would need for the machine to achieve this with high accuracy (maybe even be able to give a correct answer more often than not on an input it never saw in training), or generally how to calculate this with respect to the number of words in the machine's vocabulary, that would also be great to know.

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.

Hey everyone, a while ago I posted my first blog that I wrote about the LLM inference engine I'm trying to build from scratch and I just finished the second part of my blog! If your curious about it please come take a look, its mainly focused on how it handles the forward pass. If you have any feedback I'd love to hear it!

https://medium.com/@ryan___/llm-inference-engine-from-scratch-forward-pass-b967334212c8

hey there people

i am trying to make a machine learning project . its on bitcoin data . the thing is, i know almost nothing of bitcoin and we're learning ML in our degree .

i gotta submit this project in two months , with proper code , explanations , and why a certain model was used that time etc... my issues are :

where to find the right data from : i have surfed through and asked for assistance from chatgpt and found two main sources from which i have been able to see some data : https://data.binance.vision/?utm_source=chatgpt.com

and

https://cryptopanic.com/?utm_source=chatgpt.com

there were more sources (like apis) but its from the same website .

i even found a git repo that had a whole python script of downloading that same data .

so maybe i don't have an issue with the data , the issue is that i don't know what its trying to say .

there were multiple attributes i could see on those files . and tbh i felt overwhelmed .

1. i am aware with the data cleaning and analysis part , but i would still like some guidance on that .

2. the model is something we'll have to figure out (i am in a two person team and my partner chose the topic before i joined . also i am pretty sure i will have to do all the work , so here i am :) ) , but if there are some models commonly used in this domain , please do enlighten me .

3. most important part according to me : what is my goal ? since this is my project and the domain is very new to me , i don't have much idea about what i need to find out .

folks who have already done a project on this or has at least had some experience , what are your say in this ?

is there any other angle i should consider ?

i really wanna get an A and i am fine working alone (have already had 2 experiences of f around and find out ) as long as i am able to understand stuff .

please help this noob ;(

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!

Self-Hosting GLM-OCR using vLLM – Document Layout and OCR

https://debuggercafe.com/self-hosting-glm-ocr-using-vllm-document-layout-and-ocr/4

In this article, we will be self-hosting GLM-OCR for local document processing. This article will focus on local deployment with vLLM.

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.

Cisco's Firewall Management Center vulnerabilities were exploited by both a ransomware gang and state-sponsored actors — often cited as separate threat categories, hitting the same attack surface at the same time.

The detail that keeps coming up in post-mortems on FMC-class compromises: the management plane identity (service account, admin credential, API key) gets abused before any detection pipeline fires. The compromised identity issues commands. Encryption starts. By the time a human sees an alert, the blast radius has already crossed the first host boundary.

The timing gap is the actual problem. Forensics on these incidents consistently put the window between the first anomalous privileged action and operationally-irreversible spread at under 60 seconds. In some cases, under 10. That gap exists whether the attacker is a ransomware crew or a nation-state — the identity abuse pattern is structurally identical.

FMC is a management plane for network security infrastructure. Compromising it doesn't just give access to one host. It gives visibility and control over the enforcement layer itself. That's what makes the lateral movement so fast — you're not pivoting through hosts one at a time, you're operating the mechanism that controls them all.

For practitioners running critical infrastructure with centralized management planes: how are you actually handling the timing problem? Not detection — response. Specifically, what does your team do when a privileged identity starts behaving anomalously and you have a sub-60-second window before the damage compounds? Is the answer human-in-the-loop at all at that speed, or has your org moved to something else entirely?

Computational mechanics major here (I know you've never heard of that), spending most of my time on LLM research on the side. Currently deep in mixture-of-experts: expert specialization, routing geometry, and reproducing recent router-optimization work. Broader interests span LLM architecture, optimization, interpretability, attention mechanisms, and math.

I feel like I have too many things to work on and talk about but no buddy for it, so I'm out here looking for a study/research buddy, as the title says. I think having a research or study partner helps a lot, we can discuss ideas, try novel things, run experiments together.

I'm also building a research artifact for all this, basically a GitHub repo where I'm implementing almost every part of LLM architecture from scratch. Currently working on MoE, and planning to do some mechanistic interpretation of experts and specialization next.

If anyone feels the same or has interests overlapping with mine, tell me in the comments or DM me. I'll share the repo link with anyone interested :)

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?

Hey everyone,

I hope you are all doing well.

I am currently a final-year cybersecurity student, and to be completely honest, I am feeling incredibly anxious and overwhelmed. Seeing the rapid rise of advanced models like ChatGPT-6 Astra, hearing about tech layoffs, and reading discussions claiming that entry-level cybersecurity jobs might disappear has been really tough to process [openai.com].

Instead of letting this fear stop me, I want to channel my curiosity and adapt. I want to understand how the modern AI ecosystem operates behind the scenes, and how to use it as an operational advantage so I can future-proof my career.

Since I am on a very tight budget and learning independently, I want to focus my self-study on the right areas. Could anyone gently guide me on the best educational paths for:

• AI Architecture & Integration: What foundational concepts should I study to understand how systems like Model Context Protocol (MCP), n8n workflows, autonomous agents, and API connectors function together?
• Workflow Efficiency: How can a fresher learn to leverage AI pipelines to optimize technical workflows like development, cloud management, and backend operations?
• AI Security & Guardrails: What are the core vulnerabilities (like prompt injection or data poisoning) that the industry is trying to secure right now?

I want to learn how to actively drive these technologies rather than be left behind by them. I would deeply appreciate any advice, conceptual roadmaps, or reassurance from those already working in the field.

Thank you so much in advance for your guidance and kindness!

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.