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A general video on causality for non-specialists - feedback welcome
▲ 23 r/learnmachinelearning+7 crossposts

A general video on causality for non-specialists - feedback welcome

I made a NeuralCipher video introducing causality for a broader AI/science audience.

The goal is not to present a technical tutorial on causal inference, but to make the conceptual distinction clear: association, intervention, counterfactuals, explanation, and why causal claims require more than predictive success.

I tried to avoid the shallow version of “correlation is not causation” and instead explain why causal reasoning changes the kind of question we are asking.

Disclosure: I made this. I would especially appreciate corrections from people working directly in causal inference.

▶️ https://www.youtube.com/watch?v=dzgwW2n19bE

See more at neuralcipher.net

What is the most common misconception about causality that you see outside the field?

u/NeuralCipher_NC — 8 hours ago
▲ 3 r/learnmachinelearning

How much volume of specific mathematics for AI, and machine learning is needed for better intuition

Hello everybody, I have pending question that has been long standing for quite some time now, and I'm sure it's mostly every beginner question in this field? What math is really required now, my understanding is that AI heavily relies on these three math foundation( probability, linear algebra and calculus.... now I've seen so many reddit post on this question and yet I've not found an answer that really is ordered or in some sense is a complete guardrail in the math foundation for machine learning, it's either half baked, vague or just not good enough, I've searched different YouTube queries regarding this and most, if not all the courses don't take account to the person background knowledge like their teaching style , don't get me wrong it's not easy I mean stepping in to a math foundation field with someone from a medical or non stem related background but the willingness won't fade, cause it's something I really am awed by and fascinated by how human brain inspired such thinking model. So my last hope is to plead with the great AI and machine learning experts that have stood the test of time , if you can carefully curate a list of relevant topics that you think is core to the learning process, each topic from linear algebra, calculus and probability, I have no problem with how long it may take, I just want to actually put my hands in dirty and get the feeling of building projects or solving problems using these foundation tools, please I beg you, i know it's a lot asking experts from a strange like myself but the dedication is unwavering and the thirst for knowledge is an all time high.

Thank you..

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u/Excellent-Pension455 — 4 hours ago
▲ 25 r/learnmachinelearning+1 crossposts

I implemented YOLO26n inference from scratch in ARM64 Assembly + C on Raspberry Pi 4

Hi everyone,

I implemented the inference stage of YOLO26n completely from scratch using ARM64 Assembly and C, targeting the Raspberry Pi 4 (ARM Cortex-A72). The goal was not just to run a neural network, but to understand and reproduce many of the low-level optimizations used in modern ARM inference engines.

The project includes:

ARM NEON SIMD vectorization

Winograd Transform F(2×3) convolution

Optimized GEMM kernels

Cache-aware tiling

Custom ARM64 micro-kernels

Operator fusion

Pointwise and depthwise convolutions

Float32 inference pipeline

Implementation of YOLO26 components such as Conv, C3K2, SPPF, C2PSA, PSA, BottleNeck, and Detect

I also benchmarked different kernel designs and optimization strategies on Raspberry Pi 4 and compared their performance.

Repository:

https://github.com/mohammad-ghaderi/YOLO26

While the implementation works correctly and produces good detection results, the performance is still lower than I expected.

If you have any ideas, suggestions, or questions, I’d really appreciate hearing them.

Thanks for taking a look!

u/Forward_Confusion902 — 10 hours ago
▲ 19 r/learnmachinelearning+1 crossposts

What would make an ML curriculum for SREs actually useful day-to-day?

I got tired of ML tutorials that teach through flowers and passenger manifests.

https://github.com/laban254/ml-for-infrastructure

As someone who spends time looking at dashboards, digging through log files, and getting paged at bad hours, I wanted to learn ML through problems I actually face, not toy datasets. So over the past few months, I put together a curriculum of 27 Jupyter notebooks, all framed around real observability and SRE scenarios.

A few examples: Isolation Forest anomaly detection on synthetic Prometheus metrics with real daily seasonality (with a slider to see how the contamination parameter changes alert volume, and a Z-score comparison to show why static thresholds miss seasonal anomalies). Log clustering with TF-IDF + KMeans that auto-names clusters from keywords and flags novel patterns it hasn't seen before. KS-test drift detection for when a production distribution has permanently shifted. A PyTorch LSTM that does recursive forecasting with a preemptive capacity alert. MLflow tracking for a full hyperparameter sweep with inline run comparison. And a small LoRA fine-tune that turns raw log lines into structured JSON.

Genuinely curious what people who actually do this job think: what production scenarios am I missing that would be worth adding? Does this kind of framing (real infra data instead of toy datasets) actually help build intuition, or is it a gimmick?

u/kibe254 — 12 hours ago
▲ 51 r/learnmachinelearning+7 crossposts

We're building agents that can read millions of documents, but still forget a video they watched yesterday.

One thing has felt odd to me while working with AI agents.

We've gotten pretty good at giving them memory for text.

They can search documentation, index repositories, retrieve past conversations, and even build long-term memory over time.

Videos, though, are still treated as temporary input.

The agent watches a recording, answers a few questions, and when the session ends, that understanding is usually gone. Next session, the same video gets processed all over again.

That feels like an architectural gap rather than a model limitation.

A video isn't fundamentally different from any other source of information. Once you've extracted transcripts, OCR, visual observations, and timestamps, why throw that work away?

I ended up building an open-source project around this idea.

Instead of asking the agent to repeatedly "watch" the same video, it builds a persistent local index the first time. Future questions become retrieval instead of video analysis.

It changed how I think about video in agent workflows.

I'm curious whether others see this as a real missing piece, or if you've already solved it another way.

GitHub: https://github.com/oxbshw/watch-skill

u/Fearless-Role-2707 — 16 hours ago
▲ 6 r/learnmachinelearning+2 crossposts

I built a free desktop app to make running local models dead simple (Athanor Lite)

Been lurking here for a while and using local models for client work. Kept hitting the same wall, the people who would benefit most from local AI are the ones least equipped to set it up.

So I built Athanor Lite. Free, open source, Windows app. Here's what it does:

- Scans your hardware and tells you what you can actually run

- Browses a model catalog with size/quant filters

- Manages your local models (detects existing Ollama installs too)

- Runs inference with a real-time HUD showing tok/s, GPU %, VRAM, temperature

- Workspace system so you can have different models/contexts for different tasks

- Engine start/stop/restart controls so you're not guessing if the model is running

Built with Tauri 2 (Rust backend, React frontend). It wraps llama.cpp for inference. No cloud, no telemetry, no accounts. I don't even have analytics on the website.

This is v0.1.1. It works, it's signed, but it's early. I'm a solo dev so feedback is genuinely useful. Things I know are coming: Mac support, model comparison view, fine-tuning support.

GitHub: https://github.com/BBALabs/athanor-lite

Download: https://github.com/BBALabs/athanor-lite/releases/latest

u/BBASecure — 18 hours ago
▲ 4 r/learnmachinelearning

I know machine learning because it was in my academics. But not proper notes, does any I have handwritten notes (digital) ?

Please share notes I’ll be very grateful. It also helps others. If you have please let me know in comments

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u/bunny-arc — 11 hours ago
▲ 0 r/learnmachinelearning

Anyone else noticing this?

Feels like every AI project starts with choosing a model...

...and ends with figuring out GPUs, inference, latency, and cloud costs. 😅

Was this your experience too, or am I the only one?

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u/Faiz_123_ — 11 hours ago
▲ 100 r/learnmachinelearning+3 crossposts

[VisualTorch] How to generate architecture diagrams from PyTorch models

I built a small tool to auto-generate architecture diagrams directly from PyTorch models, which I originally built for my own research paper.

26k+ PyPI downloads, already used in publications (Nature, IEEE, MDPI), check out some use cases here: https://visualtorch.readthedocs.io/en/latest/markdown/showcase/index.html

It traces an actual forward pass, so it correctly captures branching, skip connections, and multi-input models, not just flat sequential stacks.

import visualtorch
import torchvision.models as models

model = models.resnet18()
img = visualtorch.render(model, input_shape=(1, 3, 224, 224), style="graph", show_neurons=False, layer_spacing=60)
img.save("resnet18.png")

Three rendering styles depending on what you want to show:

  • graph: node/edge diagram, good for showing branching/skip connections clearly
  • flow: stacked volumetric boxes, closer to the classic CNN-paper look
  • lenet: the classic LeNet stacked-plane style

GitHub: https://github.com/willyfh/visualtorch | Docs: https://visualtorch.readthedocs.io/en/latest/

Open to feedback, especially if you hit a model it renders weirdly :)

u/LostDistance9365 — 20 hours ago
▲ 139 r/learnmachinelearning

Anyone have this book - hands on ml with scikit learn and pytorch

So as per your recommendation guys - does anyone have this book i want it and with the original book length of pages

And I also had a question I am starting to learn ml so first I complete the maths or learn simultaneously?

u/Upper_Tip7435 — 1 day ago
▲ 3 r/learnmachinelearning

First ever job interview in 2 days for an ML role. how cooked am I?

So I got an AI-conducted interview in 2 days for a Machine Learning Researcher position. First interview ever, not sure what to expect but they didn't asked for experience and is a contract job.

My background: I've built and trained deep learning models including a Dual-Branch LSTM trained on X years of climate data to forecast variables across geographic regions. I also built a full production system around those predictions, a crop simulation engine that uses the forecasts day by day. On the LLM side I've implemented a conversational assistant with tool-calling architecture and RAG with pgvector for semantic search over certified domain knowledge.

Im currently working on updating that project but the updates aren't exactly useful in a machine learning point of view i guess, mostly develop more models with different and more specific data.

I know TensorFlow/Keras well enough to design, train and evaluate models. I can handle data preprocessing, scaling, custom loss functions, and I understand what I'm doing and why. Python is decent but not competitive-programming level, I can implement basic data structures but I'm not a LeetCode grinder.

For the interview I'm expecting:

  • General ML theory questions (linear regression, loss functions, gradient descent, neural networks, backpropagation)
  • Questions about the current AI landscape and innovations.
  • A practical exercise: probably define a model, prep some data, explain your choices

My concern is the theory side. I know these concepts from applying them but explaining them mathematically on the spot in English (not my first language) is a different story.

How cooked am I realistically? And what should I actually expect from this kind of interview? Any advice on what to brush up on in 2 days? I didn't even expected to be choosed so i just applied and now got an interview in 2 days.

I didn't even know they did AI-assistant based interviews now.

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u/Less_Measurement8733 — 17 hours ago
▲ 5 r/learnmachinelearning+1 crossposts

Learning ML by “ hands on ML with scikit-Learn” by O’rielly 3rd edition

Guys I am trying to learn ML and also wanted to be an ML engineer so should I study this book or suggest me any other books

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u/_Harshan — 22 hours ago
▲ 7 r/learnmachinelearning

How do I get into practical aspect of ML

​

1st yr ended 1.5 months ago and I Have been following ML for past week. Attended three lectures of Andrew Ng - covering Linear, logistic regression. While I am getting the theoretical and mathematical aspect of ML via his lectures, I have absolutely no idea on how to get hands dirty ,practical learning, what projects to make, the required python libraries, etc. Please suggest a good guide for that.

P.S : I know basic python(not OOPS in python if required), numpy, pandas, matplotlib & Seaborn.

My ultimate goal by next year is to publish some paper in A* ML conf, and apply for Research Interns in top global uni for next summers. Sincere advice needed. Critics appreciated too.

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u/Ill_Trick8969 — 21 hours ago
▲ 18 r/learnmachinelearning

Beginner to ML: What's the correct way to set up Python, virtual environments, Jupyter, and libraries?

Hi everyone,

I'm starting my Machine Learning journey, and I come from a non-technical background. My previous experience is in business, so I'm learning everything from scratch.

What confuses me isn't Python itself—it's setting up the development environment correctly.

I want to understand the best workflow for learning ML:

- How should I install Python on Ubuntu?

- Where should I install Jupyter Notebook?

- How do virtual environments actually work?

- Should I create a new virtual environment for every project or use one for learning?

- If I install libraries like NumPy, Pandas, Matplotlib, Scikit-learn, or TensorFlow, should I install them globally or inside a virtual environment?

- If a library is already installed on my PC, do I need to install it again for every virtual environment, or can VS Code/Jupyter use the same installation?

- How do VS Code and Jupyter detect the correct Python environment?

- What's the best folder/project structure while learning ML?

- Are there any beginner mistakes I should avoid?

I'm not looking for just commands—I want to understand how experienced ML engineers set up and manage Python, environments, libraries, VS Code, and Jupyter so I can build good habits from the beginning.

I'm using Ubuntu, if that makes any difference.

Thanks in advance!

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u/Upper_Tip7435 — 1 day ago
▲ 0 r/learnmachinelearning+1 crossposts

I'm 15 and built a self-learning neural network from scratch in NumPy — per-neuron attention, forwar

I built ONA — a self-learning neural network entirely in pure Python + NumPy. No PyTorch, no TensorFlow, no GPU, no cloud API.

Key innovations:

- Per-neuron attention: every neuron has its own Q/K/V/O weights

- Forward-pass learning: no separate backward pass, learning happens during forward

- Self-discovered subword tokenizer: vocabulary grows during training

- Sparse routing: only 3-5 neurons activate per query

4.4M parameters. Runs on Raspberry Pi Zero. Continuously learns from Wikipedia and conversations.

Full story: https://medium.com/@kasishgadadhasu13/im-15-i-built-a-self-learning-neural-network-from-scratch-no-frameworks-no-gpu-e460f06c6599

I'm 15 years old, class 10 student. Happy to answer questions.

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u/Whole_Bridge3064 — 1 day ago
▲ 10 r/learnmachinelearning

Is there any ML/AI bootcamp open to students from all around the world that ACTUALLY teaches you all the skills companies look for from scratch without pre-requisite uni degree?

Premise:

  1. I constantly see people saying they have x,y,z knowledge but they were rejected because missing something companies are looking for.

  2. It also seems like organizations need increasingly need ML skills and as fast as possibile.

  3. The number of uni graduates in these specializations is always very low compared to the potential demand and orgs always will complain those graduates don't know all the skills and tools they would need in a full time job.

Be it real or fake, seems like we have a disconnect here between supply and demand and I can't believe there's no one who successfully built an end-to-end curriculum for a (free or paid) bootcamp to profit from this disconnect, the same way swe/coding bootcamps contributed creating employment in the past (now dead due to AI/shifts in tech hiring).

By "successfully" built I mean "consistently works in taking people from 0 to hired"

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u/Ordinary-Round-8107 — 1 day ago