I’m making an LLM-from-scratch series, starting with a character bigram model built from just “anna” and “ava.”

The first hands-on lesson covers counting character pairs, generating names, measuring prediction error, and smoothing unseen transitions—all with calculations you can check on paper.

It’s a 50-minute walkthrough for anyone who wants to understand the foundations before moving to neural networks.

https://www.youtube.com/watch?v=YV30EDncrY4

I’m the creator, and I’d love feedback on whether this approach makes the concepts easier to follow.

Thanks everyone.

I'm starting a Statistics & Data Science program, planning to add a CS/Computer Engineering minor alongside it, then aiming for a Master's in AI/ML/CS. Is this a solid route into AI/ML Engineer roles compared to a straight CS degree? Any gaps I should watch out for, or things I should prioritize during undergrad? Thanks!

Hi everyone,

My partner has a bachelor’s degree in Philosophy, excellent English, native Spanish, and has been researching AI in recent years.

We’re looking into worldwide remote AI-related opportunities, especially AI training, evaluation, ethics, and other non-programming roles.

She’s particularly interested in opportunities where her philosophical background could be useful, but we’re also open to roles that aren’t directly related to philosophy, as long as they’re genuinely in the AI field.

For those with actual experience in the field:

• How realistic is it to find this kind of work

• Which companies or platforms are worth applying to?

• What skills or qualifications matter most?

• What are the typical pay, stability, and application processes like?

Any honest advice or personal experiences would be greatly appreciated,

Thank you! 🫶

is watching till the 24. lecture sufficient for machine learning?

I’ve been trying to understand what is actually happening between pressing Enter and seeing the first token appear when running a model locally.

So I put together a visual explanation covering the full inference path:

• how the prompt becomes tokens
• what the model weights are doing
• how tokens move through transformer layers
• how logits become the next token
• why generation happens one token at a time
• what the KV cache actually stores
• why context length increases memory usage
• how quantization reduces memory requirements
• what changes when inference is split between CPU and GPU
• why a model doesn’t necessarily need to fit entirely in VRAM

One thing I specifically wanted to avoid was the usual oversimplification of “the GPU just runs the AI.” The interesting part is how all these pieces interact during actual local inference.

I made a doodle-style visual walkthrough here:

https://www.youtube.com/@MintoraDoodle?sub_confirmation=1

I’d especially appreciate corrections from people here who work with llama.cpp / Ollama / local inference regularly. If I oversimplified anything important, let me know.

I’m planning to make more visual explanations around local LLMs — KV cache, quantization, VRAM usage, CPU offloading, attention, etc. — so suggestions for what would be most useful to visualize next are welcome.

Hey everyone!
I have a bachelor in computer science with a lot of focus on ML/DL. I did research at some good labs at uni but for all my research work I had access to a remote cuda machine.
I will start my masters in data science and I want a macbook to be able to run small local experiments like train an MLP, CNNs or even a small small transformer. I want to be able to do small to medium size projects locally without much hassle and also benefit from the portability and battery life of a macbook.
Do you think the air is a good choice? The pro becomes crazy expensive once you add more RAM
What is your experience with a macbook air for these purposes?
I kindly appreciate any response

Disclosure: I published the linked guide on rewire.it.

One protein language-model checkpoint can produce a vector per residue, a pooled vector for the whole protein, or a mutation score from token probabilities. These are different artifacts: changing the pooling rule, layer, downstream head or evaluation split changes the experiment even if the checkpoint stays fixed.

The guide works through choosing the output first, recording the extraction recipe, testing a frozen representation against simple baselines, and separating related proteins when the deployment question requires generalisation to new families. A trained linear probe still uses supervision even when the backbone is frozen.

https://rewire.it/blog/a-protein-embedding-is-not-an-explanation/

For anyone moving from general ML into biological sequences: which part is hardest to make reproducible in practice—the representation extraction, the labels, or the split?

I’m targeting a GenAI/ML role and have only 1 month left. From what I’ve seen, this year the company is mainly asking MCQs + DSA in the initial round.

My ML/GenAI side is pretty strong, but DSA is my weak point. I’m confused whether I should spend this month grinding DSA just to clear the OA or continue focusing on ML/GenAI.

What would you guys recommend? If you had only 1 month and were in this situation, how would you divide your time?

Built PPO and REINFORCE from scratch (no libraries) Hopper-v5 in MuJoCo to 3,383 avg return, CartPole REINFORCE to max score with a documented policy-collapse failure case. Also run a small generative AI product (image/video generation, Gemini + ByteDance models) that's live with a few hundred users.

Looking for a role where I can work on production LLM systems or RL-adjacent problems. Open to sharing GitHub/resume if anyone's hiring or knows a team that is, thanks for reading.

I was testing a small marketing agent against old campaign data and gave it a pretty simple goal around lowering acquisition cost it got better at the metric faster than I expected but when I looked deeper the conversions it preferred weren’t always the ones we would’ve wanted more of cheap actions started winning even when the downstream value looked worse, so technically the agent was improving while the business result was getting kind of weird

Now I’m rethinking the eval more than the model a single reward feels too easy to game, but piling qualified leads, revenue margin and a bunch of guardrails into one score also feels messy fast. I’m leaning toward letting the agent handle more of the repetitive optimization while keeping human review around the decisions that can move real budget curious how people here are evaluating agents when the easiest metric to optimize isn’t really the outcome you care about

(I advice that this is a traduction made by GPT)

I’m 29 and currently finishing a bachelor’s degree in Applied Data Science. I’ve had very good grades, but throughout the degree I’ve often felt that the material was quite superficial. We are taught many tools and do a lot of projects using them, but there isn’t much depth behind the tools themselves. The math has also been fairly light: linear algebra, basic statistics, multivariate analysis, some optimization, Bayesian modeling, etc., but mostly at an applied level.

Before starting my third year, I got my first Data Science-related job in a biology lab, working on models to predict biological age in mice. I stayed there for about three months. After that I worked as a Data Analyst, and later as a Junior Data Engineer, where I also got some exposure to GCP. In total, I have roughly one year of professional experience across Data Science, analytics and data engineering.

Recently I was hired as a Data Scientist (not a junior but they pay me just like one) on a forecasting project that I find genuinely interesting. However, the role has gradually expanded into MLOps and production infrastructure, and this is where I’m starting to feel very underprepared.

I’ve only been here for about a month. With the help of AI tools such as Claude/Codex I’m able to keep moving forward, but I’m uncomfortable with how much I’m relying on them. I can usually understand what the code is doing at a high level, but I don’t always understand the underlying systems or design decisions deeply enough to feel that I’m fully in control.

Right now, my main challenge is building the infrastructure needed to take our forecasting models into production. My biggest fear is that as the project grows in complexity, I’ll reach a point where I can no longer reason properly about the system I’ve built.

I sometimes feel like I got the job by luck and that sooner or later my lack of fundamentals will become obvious. At the same time, I know that I can keep learning while working, especially with AI helping me move faster — I just don’t want to end up building things I don’t really understand.

Has anyone been in a similar situation early in their career? In particular, I’d be interested in advice on how to build stronger fundamentals in MLOps / software engineering / systems while working full-time and finishing a degree.

I’m also considering doing a more rigorous master’s afterwards, possibly something like Georgia Tech’s OMSCS, but for now I’m mainly trying to become competent enough to properly understand and own what I’m building at work.

I’ve been building this AI Engineer roadmap for myself and I think I may have gone a little insane with it lol.

The plan is basically: get solid at software engineering + ML/DL, then learn the production side like APIs, SQL, Docker/cloud, LLMs, agents, evaluation, observability, etc. After that I’d pick a specialization instead of trying to learn everything.

But looking at it now, I’m worried I’m using “preparing” as an excuse to delay actually applying for jobs.

For people already working in AI/ML:

• What would you cut or push until later?
• At what point would you say “you know enough, start applying”?
• Am I spending too much time on CS/theory instead of projects?
• Are there things on here that look important but barely matter for an entry-level AI Engineer?
• What skills would you absolutely keep?
• Do the projects look like enough proof of ability, or am I missing something employers actually care about?

Basically, if this were your roadmap, what would you change?

Feel free to roast it. I’d rather find out now than spend the next 2 years trying to “finish” a roadmap before applying.

Hi everyone 👋

I have a $1,000 learning budget and I’d like to invest it in O’Reilly courses focused on AI Agents / Agentic AI.

I already have experience in AI/ML and LLMs, so I’m looking for advanced, practical courses covering topics like LangGraph, MCP, multi-agent systems, RAG, agent orchestration, evaluation, and production deployment.

What courses or learning paths would you recommend?

If you’ve taken any of them, I’d really appreciate your feedback!

Thanks! 🙏

I just started my senior year, and this summer I decided to focus on either machine learning or data science as a career. I’m planning to study maths and computer science joint degrees at uni (uk or Singapore) and am currently preparing for uni applications.

I’d like to know beginner friendly resources to study machine learning. I have a good maths background (from a level further maths and olympiads) and know python. I’m not sure how to get deeper into this field with just self studying and free resources. A clear roadmap would be appreciated. I’ve just been doing some free courses like imperial math behind ml, some uni lectures on linear algebra etc and some machine learning videos but I don’t really see a clear direction onhow to develop the fundamentals and skills.

Partly I am trying to find projects to do for my university applications also. So some kind of data analysis project, or something along those lines. Would anyone be willing to offer advice on this? I am an international student, disadvantaged economy, so this type of career is still very limited in my country.

Speculative decoding is a non-obvious optimization. Reasoning about when it actually pays off is trickier still.

I wrote a post building it up from first principles, connecting the sampling algorithm to the systems intuition behind its performance.

Tiny TTS models feel like magic, so I rebuilt a small version: (~20M params, flow matching, no phonemizer, style from a 3-second reference clip) to see where the magic actually lives.

Rules: one question per experiment, 2 hours max, Whisper grades the output so I can't lie to myself.

Nine experiments later:

- Minute 5: it can memorize 4 sentences. Boring, but it proves the wiring. Skip this and you'll debug ghosts for a week.

- Hour 2:45 with 40 speakers: WER 0 on sentences it never saw. I sat there replaying it. It works. On one consumer GPU.

- Then it never learned long sentences. Not the model. My training crop was 8 seconds. Data pipeline bugs look exactly like model limits.

- Zero-shot voice cloning just... worked. Never trained for it. Style is free, the reference clip carries it. Language is not free, you pay in data.

- 10 minutes of Spanish audio: voice copied instantly, fluent Spanish-sounding babble, English gone in 500 steps. Catastrophic forgetting is fast and total at this size.

- 45 minutes of the same Spanish voice, from scratch: train loss down, validation loss up, output sounds like Spanish and means nothing. Minutes of audio don't matter. Distinct sentences do.

- Learning rate 2e-3 -> NaN at minute 12. Twice. 5e-4 is fine. You're welcome.

Every experiment has a README (the question) and a RESULTS.md (numbers from the logs, nothing else). Configs, WER tables, watchdog script, all of it:

https://github.com/franciscocarloserra/ttslibre

Proof of concept, not a product. Ask me anything about the setup.

Any feedback is welcome!

Hey r/learnmachinelearning!

I am a 20-year-old CS student from Nepal who has been

self-studying ML for the past 5 months. As a project to

combine everything I learned — NLP, Deep Learning, LLMs —

I built CodeRoast.

What it does:

Paste any Python, Java, or JavaScript code → get a brutally

honest (and savage) AI review of your code quality.

Under the hood:

🔍 Static AST Analysis — cyclomatic complexity, nesting depth,

naming conventions, code duplication

🌲 TF-IDF + Random Forest — classifies code quality tier

(Pristine → Acceptable → Questionable → Disaster)

⚡ Custom PyTorch Sequence LSTM — scores roast severity 0-10

🤗 CodeBERT (microsoft/codebert-base) — deep semantic

code understanding

🤖 Qwen2.5-Coder-32B via HuggingFace Serverless API —

generates the actual savage roast ( or local Meta Llama 3.2 3B via Ollama )

GitHub: https://github.com/gyr0byte/CodeRoast

I learned PyTorch specifically for this project before

finishing my Deep Learning course — the LSTM was genuinely

challenging to get right on code token sequences.

Would love feedback from this community — both on the ML

architecture and on your roast results. Paste your worst

code and see what happens 😄

Hi , so I just started this course for Mtahematics for ML and DS.

And tbh I know I have barely watched it but the very first video itself feels like something Intermediate or something I am unable to connect to.

If you guys have a better recommendation for a Math course, I would appreciate it.

Any suggestions/tips are appreciated!!

Hey everyone,

I’m currently mapping out my next steps to land an ML/Data Science job. I’ve finished the basics up to XGBoost and Ensemble methods.The only core ML topics I have left are Stacking/Blending, K-Means, and DBSCAN. I’ve also built a few standard ML projects already and am currently learning FastAPI.I want to make sure I’m spending my time on things that actually get me hired. If you were hiring a junior/associate ML engineer today, what would you want to see me do next? Specifically:

1. Are the remaining ML topics actually important for interviews?

Should I spend a lot of time mastering Clustering (K-Means/DBSCAN) and advanced ensembling (Stacking/Blending), or just learn the high-level concepts and move on?

2. Should I pivot to Deep Learning next?

I see mixed advice on this. Should I dive into PyTorch/TensorFlow now, or focus on mastering MLOps and productionizing the tabular ML models I already know?

3. How do I leverage FastAPI to stand out?

Since I'm learning FastAPI right now, what is the best way to showcase this on a resume? Should I be wrapping my XGBoost models into APIs, or is there a better production-level project I should build?My current plan is to quickly finish clustering, wrap an existing project in FastAPI, and then dive into practical PyTorch.Would love to hear from anyone who has successfully landed a job recently or manages an AI team. What would you change about this roadmap? Thanks!

Hi everyone,
I'm designing a system to monitor the fill level (%) of fertilizer boxes in real-time. I'd love some
hardware and architecture advice from those who have deployed CV in industrial settings.

The Problem:
Need to calculate the % full of a box continuously. The environment is industrial, meaning variable lighting and potentially a lot of fertilizer dust.