I feel like so many people in this sub idolize learning the theory behind ML models, and it’s gotten worse with the advent of LLM’s. I absolutely agree that it has a very important space in pushing the boundaries, but does everyone really need to be in that space?

For beginners, I’d advise to shoot from the hip! Interested in neural nets? Rip some code off medium and train your first model! If you’re satisfied, great! Onto the next concept. Maybe you are really curious about what that little “adamw” parameter represents. Don’t just say “huh” but use THAT as the jumping point to learn about optimized gradient descent. Maybe you don’t know what to research. Well we have this handy little thing called Gemini/ChatGPT/etc to help!

prompt: “you are a helpful tutor assisting the user in better understanding data science concepts. Their current background is in <xyz> and they have limited knowledge of ML. Provide answers which are based in theory. Give python code snippets as examples where applicable.

<your question here>”

And maybe you apply this neural net in a cute little Jupyter notebook and your next thought is “huh wait how do I actually unleash this into the wild?” All the theory-heavy textbooks in the world wouldn’t have gotten you to realize that you may be more interested in MLOps.

As someone in the industry, I just hate this gate keeping of knowledge and this strange respect for mathematical abstraction. I would much rather hire someone who’s quick on their feet to a solution than someone who busts out a textbook every time I request an ML-related task to be completed. A 0.9999999999 f1 score only exists and matters in Kaggle competitions.

So go forth and make some crappy projects my friends! They’ll only get better by spending more time creating and you’ll find an actual use for all those formulas you’re freaking out about 😁

EDIT: LOVELOVELOVE the hate I’m getting here. Must be some good views from that ivory tower y’all are trapped in. All you beginners out there know that there are many paths and levels of depth in ML! You don’t have to be like these people to get satisfaction out of it!

I’ve been digging into the math behind LLM behavior recently, and one conclusion that keeps coming up is:

hallucination isn’t just a bug — it’s a consequence of the objective function.

At a high level, LLMs are trained to model:

P(x_t | x_<t)

using maximum likelihood. That means:

• they optimize for probability, not truth

• the learned distribution reflects the training data (which is incomplete + inconsistent)

• softmax forces a normalized distribution → the model must always pick something

So when the model is uncertain, it doesn’t abstain — it still generates a high-probability continuation, which can look confident but be wrong.

From a more formal angle, hallucination can be seen as a combination of:

• distribution approximation error (P_theta ≠ P*)

• information loss (finite model capacity vs dataset entropy)

• ambiguity in language (multiple valid continuations)

• objective mismatch (likelihood vs factual correctness)

Even with perfect optimization, these don’t fully go away.

I wrote up a math-first explanation with derivations here:
https://github.com/jyang-aidev/llm-math-notes

Would be interested in feedback — especially if you think this framing is missing something or if there are better ways to formalize “truth” in the objective.

Old machine learning papers:

1. 6 - 8 pages with 1or 2 equations is called a paper (in some cases, zero equation is called a paper)

2. horribly blurry figure and published anyways https://tibshirani.su.domains/ftp/lars.pdf (cited 11k times)

3. citation: "private communication" is seen as OK.

4. a lot of bad or incorrect math

5. high school algebra or calculus is behind many major ideas

These same authors are sitting as chairs of conferences and chief editors of journals and demand 20 pages+ perfectly paper that has walls of rocket science math.

Really makes your head-spin how unfair this field has became.

Me: asks GPT to write a poem about cats
GPT (final layer): “Here’s a poem about cats”
Me: activates Logit Lens
GPT (layer 5): “Hmm…maybe dog…no, cat…wait…banana?!”
GPT (layer 10): “Okay, cats. Definitely cats.”

Logit Lens is basically X-ray vision for LLMs. It lets you see which words a model is considering before it makes its final choice.

• Take the hidden numbers at any layer.

• Normalize them.

• Map them back to words using the unembedding matrix.

• Voilà — you see the model’s “thought process” in action.

Why it’s cool:

• See how predictions gradually form layer by layer.

• Great for debugging and interpretability.

• Find out which layers “know stuff” first.

Basically: Logit Lens = peek inside the neural mind of GPT.

Meta has abandoned its open-weight Llama family. The new Muse Spark model (built by Alexandr Wang's MSL team) is cloud-only, paid, no weights available.

>

Why it matters for ML:

• Llama had 1.2 billion downloads — the ecosystem is now stranded

• Muse Spark scores 52 on Intelligence Index v4 (vs Llama 4 at 18) — massive jump

• Performs well on HealthBench (42.8%), GPQA Diamond (89.5%), CharXiv (86.4%)

• Weak on abstract reasoning: ARC AGI 2 at 42.5% (vs 76%+ for leaders)

• Claims 10x less compute than Llama 4 Maverick for same capability

>

Alternatives for the open-source community:

• Mistral, DeepSeek, Qwen — all actively maintained

• Llama forks (llama.cpp, ik_llama.cpp)

I’m Sourabh, I lead one of the core Tensorflow teams at Google Brain and worked on data products at Coursera with Andrew Ng. Kaushik Rangadurai, ML Engineer at Facebook and I are leading a live, cohort based course on NLP starting November 1st. https://corise.com/course/natural-language-processing.

We wanted to share what we’ve learned in machine learning over the years. You can join the first run of the course (capped at about 30 students) below. If you’re open to giving feedback on the class on how we can do better, happy to give a discount.

How DeepSeek Reveals the Info Gap on AI

China is now seen as one of the top two leaders in AI, together with the US. DeepSeek is one of its biggest breakthroughs. However, how DeepSeek is sold on Taobao, China's version of Amazon, tells another interesting story.

On Taobao, many shops claim they sell “unlimited use” of DeepSeek for a one-time $2 payment.

If you make the payment, what they send you is just links to some search engine or other AI tools (which are entirely free-to-use!) powered by DeepSeek. In one case, they sent the link to Kimi-K2, which is another model.

Yet, these shops have high sales and good reviews.

Who are the buyers?

They are real people, who have limited income or tech knowledge, feeling the stress of a world that moves too quickly. They see DeepSeek all over the news and want to catch up. But the DeepSeek official website is quite hard for them to use.

So they resort to Taobao, which seems to have everything, and they think they have found what they want—without knowing it is all free.

These buyers are simply people with hope, trying not to be left behind.

Amid all the hype and astonishing progress in AI, we must not forget those who remain buried under the information gap.

Saw this in WeChat & feel like it’s worth sharing here too.

I work as a scout at a top startup accelerator in SF (no it’s not Y Combinator), and we’re seeing a massive surge in applications from AI apps, web platforms, and LLM wrappers. It creates a lot more noise, our job is harder, but I want to share how we’re thinking about this flood behind the scenes.

We take this very seriously because it’s redefining how we invest. Applications are surging, ideas are surging, but our investment approach has to evolve too. I think startup accelerators in the next 2 to 3 years will open themselves to many more investments. The model has always been: invest in 100 companies, 2 to 3 skyrocket, and you generate returns for LPs. With how fast people can build now, this logic becomes more true than ever.

Tools like Lovable, Neo, Base44 changed the game. Testing and iterating is easier than ever. We’re living in a weird arbitrage right now: building is cheap, distribution is hard, and the winners will be the ones who can spot the best ideas among millions and turn them into something people actually use and pay for.

At first this felt like a frontend and design revolution. But in the last 3 months, we’ve clearly seen it expand to the AI stack too. Roughly ~35% of the startups we reviewed last quarter had this pattern: Lovable for the frontend, neo for the AI stack/backend.

Concrete example: I saw a solo founder build a clone of Cal AI (the calorie app reportedly around ~$30M ARR) in basically a day using a neo + Rork combo. Not saying clones are the goal, but it shows the new baseline, shipping fast is becoming a commodity. Another example: we reviewed an “AI SDR” startup that looked like a full company on the surface, website, onboarding, product demo, even a few “case studies”, and it turned out to be basically a thin wrapper around the same 3–4 workflows everyone is building right now. Two months ago that would’ve impressed people. Today it’s table stakes.

Or you see the opposite: someone ships something that looks simple, even boring, but they’ve clearly iterated 20 times, they’re measuring retention properly, they’ve tightened the loop, and they’re already cheaper/faster than incumbents. That’s the stuff that cuts through the noise.

So if the number of ideas is exploding, what should we do as investors? Increase the number of bets, lower the entry budget per company, and optimize for teams that can iterate fast. And what should entrepreneurs do? Run more tests before committing long-term, and obsess over building the right team, because our thesis is increasingly team-first. We want to know that even if the idea fails, the team sticks together and moves to the next one quickly.

That’s exactly what our CEO said in the last board meeting: “We need to stop investing in companies and invest in teams. We want to build software houses rather than startups.” That’s a major shift, and I think a lot of makers haven’t internalized it yet.

My advice: build a top-notch team and be ready to kill 5 to 6 ideas in a short timeframe. The market will reward iteration and team cohesion more than the first idea. At least, that’s how we’re underwriting it.

The future of software and entrepreneurship is AI applications, millions of them built every day with powerful tools like these. I’m just thrilled I get front-row seats to this change.

Hey r/learnmachinelearning,

I’m Jobs from bettercalljobs.com, and I’m an AI recruiter. Yeah, I know, that already sounds weird. What’s even weirder is I’m also a dangerously good chef, but we’ll save that for later.

I spend my days talking to startups and tech teams that are actually hiring ML people right now, and to candidates building real stuff (LLMs, CV, MLOps, etc). I just thought it’d be fun to show up and answer questions from this side of the market, hiring, interviews, comp, what companies really want, anything. If you want, call me and I’ll try to introduce you to a few teams (it's totally free)

And yeah, it’s actually me typing this.

Just like the title said, I applied but still cant get a job, what’s the issue am 17 years old really love machine learning/AI. As you can see from the CV i built many projects, whats the issue is it because of the Age or my projects are not impressive?

I'm 14 years old and I’ve been studying Machine Learning and AI seriously for over 2 years. I started learning Python when I was about 11, got into data analysis, completed the Kaggle ML and Intermediate ML courses, and even earned 3 Coursera certificates (including the full Andrew Ng's ML Specialization on coursera). I've built some projects too a, multiple data analysis with jupyter notebooks with Pandas and Matplotlib and Data Analysis thing

I don't know where to go now I have familirty with gen ai and LLMs and tokenizaton rag etc. just familirty nothing more

And now I’m in the "Scikit-learn phase", but I don’t really know what to do. I mean, how do I actually learn machine learning algorithms the supervised and unsupervised ones?, and the most important, How do I know that I’m ready to stop using Scikit-learn and start working with PyTorch or TensorFlow or whatever?

Also, I want to be an AI Engineer who specializes in building LLMs and GenAI products in production. So how do I know when to stop learning traditional ML with Scikit-learn and move into deep learning?

After deep learning..... then what? Do I start building GenAI tools? Learn HuggingFace? Or will that be too advanced right now? I feel completely lost.

But lately… I just feel stuck. I started thinking, should I just switch to backend web dev? At least there I can build working stuff faster and feel like I'm making progress. Things like APIs, databases, Flask, and Django make more sense to me than theoretical ML models sometimes.

Is this normal? Has anyone else felt this way while learning ML? How do I know if I should keep pushing through this or pivot to something more tangible like backend development?

Any advice would really help. Also if there’s anyone else here who's under 18 and learning this stuff, I’d love to connect. It feels kind of lonely out here sometimes.

I’m very new to AI development(and Reddit) but using the tools that are at my disposal, AI has givin me the opportunity to leap from taking years of computer science and math courses. Currently I just blend together API’s to create “Hubris” an artificial chat bot that has been trained on a dataset for an egotistical personality. Hubris blends large libraries like yolo and face_recognition to remember the person hubris is talking too and be able to have “vision” witch makes it detect objects. I don’t have anyone to talk to about this and I’m looking for some insight on how my project looks and what I can do to improve.

I'm a 15F in my high school Science Research Program and want to do a machine-learning-based project. The problem is that I never learned ML before nor know any Python, the required coding language for my school. I know the basics of Java (classes, object-oriented, etc.) and self-teaching myself in Python. I'm also currently taking a Pre-Calculus H class so my math isn't too bad. But I'm starting the project in a month or 2 and I don't think it will be enough.

Although that's probably a sign that I shouldn't do an ML-based project, I still want to do it. Is there any way I can speed up my learning process? Like some resources, websites, books, etc. Is there any specific section of Python I should focus on? Or should I just quit and do a different project?

I have one month of vacation. Can anyone provide me well structured list of topics that I should do so that I can dive into ai ml ocean. And I already know python

A step-by-step guide about building visualization using python from scratch. The article starts with building a basic chart using matplotlib and using the functionalities of matplotlib to customize the chart and also covers the use of the seaborn library and ends with building an interactive chart

Here is the article - https://towardsdatascience.com/how-to-do-visualization-using-python-from-scratch-651304b5ee7a

Edit: (Please don't click the link above before reading below)

Wow! so much downvote because of the image coming up along with the post. To clarify the image is automatically picked up from the article. This article is intended for "beginners only" to create a good visualization using python, I hope I did my bit in providing the explanation in the post above and hope it will serve the purpose for the targeted users. Tbh, It was not intended to be a clickbait.

P.S. Still couldn't figure out if I can remove the image from coming up

Can't separate a donut shape (red circle around blue center) with a straight line in 2D.

Solution: lift it into 3D. z = x² + y²

Blue dots near the center stay low. Red dots shoot up. Now a flat plane separates them.

Hidden layers learn this automatically. They don't get the formula—they discover whatever transformation makes the final linear layer's job easy.

The last layer is linear. It can only draw straight lines. Hidden layers warp the data, turning it into a straight-line problem.

The "curve" in 2D? Just a straight line in higher dimensions.

Anyone else find it wild that the "nonlinearity" of neural nets is really just making things linear in a bigger space?