Hands-on guides and insights and research for self-taught minds exploring the depths of ML.
This section is for the builders who learn by doing — the coders who don’t wait for permission, the late-night tinkerers turning tutorials into breakthroughs. Whether you’re retraining your first model or tuning your fiftieth, this category is designed for autodidacts ready to go deeper.
We focus on applied knowledge, not academic gatekeeping. You’ll find approachable explorations of topics like:
Fine-tuning language and vision models
Building and analyzing datasets
Experimenting with embeddings and graph-based methods
Practical Reinforcement Learning
Retrieval-Augmented Generation workflows
Toolkits for structured and multi-modal generation
All written with clarity, precision, and respect for those learning outside formal institutions.
Here at n0c, we honor curiosity as much as credentials, and we believe you should have access to advanced concepts without a degree or a gatekeeper. If you’re ready to go beyond tutorials and into deeper understanding, this section is for you.
The attention mechanism is well known for its use in Transformers. But where does it come from? It’s origins lie in fixing a strange problems of RNNs. Chapters0:00 Introduction0:22 Machine Translation2:01 Attention Mechanism8:04 Outro
Chapters0:00 Introduction1:54 Neural N-Gram Models6:03 Recurrent Neural Networks11:47 LSTM Cells12:22 Outro
An introduction to language modeling, followed by an explanation of the N-Gram language model! Sources (includes the entire series): https://docs.google.com/document/d/1e… Chapters0:00 Introduction1:39 What is NLP?2:45 What is a Language Model?4:38 N-Gram Language Model7:20 Inference9:18 Outro
https://www.youtube.com/watch?v=9-Jl0dxWQs8 AI Alignment forum post from the Deepmind researchers referenced at the video’s start:https://www.alignmentforum.org/posts/… Anthropic posts about superposition referenced near the end:https://transformer-circuits.pub/2022…https://transformer-circuits.pub/2023… Some added resources for those interested in learning more about mechanistic interpretability, offered by Neel Nanda Mechanistic interpretability paper reading listhttps://www.alignmentforum.org/posts/… Getting started in mechanistic interpretabilityhttps://www.neelnanda.io/mechanistic-… An interactive demo of sparse autoencoders (made…
If you’re interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from…
Timestamps:0:00 – Who this was made for0:41 – What are large language models?7:48 – Where to learn more
This one is a bit more symbol-heavy, and that’s actually the point. The goal here is to represent in somewhat more formal terms the intuition for how backpropagation works in part 3 of the series, hopefully providing some connection between that video and other texts/code that you come across later. For more on backpropagation:http://neuralnetworksanddeeplearning….https://github.com/mnielsen/neural-ne…http://colah.github.io/posts/2015-08-… https://colah.github.io/posts/2015-08-Backprop
The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these “nudges” in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen’s book or…
To learn more, I highly recommend the book by Michael Nielsenhttp://neuralnetworksanddeeplearning….The book walks through the code behind the example in these videos, which you can find here:https://github.com/mnielsen/neural-ne… MNIST database:http://yann.lecun.com/exdb/mnist/ Also check out Chris Olah’s blog:http://colah.github.io/His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if…
