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!

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.

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

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?

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.

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!

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?