u/Logical_Respect_2381

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Manifold hypothesis is a very interesting topic and kind of a high-level inspiration of explainable AI. It has the power of generalization both in image modality and in NLP.

In both universes, this hypothesis suggests that the enormous dimensional space in which images, for example, exist is completely sparse, except for a very, very tiny space in which all of our visuals exist.

So the probability of drawing a sample from all possible high-dimensional images and finding that sample looking like any possible known image, or even a non-complete noise image, is extremely low.

That idea suggests that all known images are kind of a manifold that the deep learning model tries to unfold.

Just like when you have a sheet of paper, which is 2D, and you write text on it, which is also 2D. But suppose you crumple that paper; then the text appears to be in 3-dimensional space, while it is not.

The role of generative deep learning is to learn this crumpled high-dimensional modality and generate meaningful samples from it.

Manifold hypothesis is a very interesting topic and kind of a high-level inspiration of explainable AI. It has the power of generalization both in image modality and in NLP.

In both universes, this hypothesis suggests that the enormous dimensional space in which images, for example, exist is completely sparse, except for a very, very tiny space in which all of our visuals exist.

So the probability of drawing a sample from all possible high-dimensional images and finding that sample looking like any possible known image, or even a non-complete noise image, is extremely low.

That idea suggests that all known images are kind of a manifold that the deep learning model tries to unfold.

Just like when you have a sheet of paper, which is 2D, and you write text on it, which is also 2D. But suppose you crumple that paper; then the text appears to be in 3-dimensional space, while it is not.

The role of generative deep learning is to learn this crumpled high-dimensional modality and generate meaningful samples from it.

A lot of beginner explanations make the journey sound like:

train a huge Transformer → release a ChatGPT-like assistant.

But a real assistant needs many layers after the base model:

base model → SFT → preference data → reward model → RLHF/DPO → safety training → chat formatting → tools → RAG → multimodality → evaluation → serving infrastructure → UX.

The attached image is one roadmap page from a 32-page visual guide I made to organize this journey in one place. The full guide also includes explanations, glossary pages, and a recommended learning path with courses/resources for each major part.

I’m mainly looking for feedback on the pipeline:

Does this look accurate for beginners?
Would you add, remove, or rename any stage?

https://preview.redd.it/jsdix48c3v2h1.png?width=1672&format=png&auto=webp&s=41388e2b21d8225f1e5f4711ba936d565d77638d

A lot of beginner explanations make the journey sound like:

train a huge Transformer → release a ChatGPT-like assistant.

But a real assistant needs many layers after the base model:

base model → SFT → preference data → reward model → RLHF/DPO → safety training → chat formatting → tools → RAG → multimodality → evaluation → serving infrastructure → UX.

The attached image is one roadmap page from a 32-page visual guide I made to organize this journey in one place. The full guide also includes explanations, glossary pages, and a recommended learning path with courses/resources for each major part.

I’m mainly looking for feedback on the pipeline:

Does this look accurate for beginners?
Would you add, remove, or rename any stage?

https://preview.redd.it/rj8grmt3zu2h1.png?width=1672&format=png&auto=webp&s=2c9a6c51cfd435443c84764643c63be3baf440db

I recently tried to make a beginner-friendly visual explanation of how Stable Diffusion works, because I noticed many newcomers hear terms like diffusion, U-Net, latent space, cross-attention, and embeddings, but often struggle to see how the full system connects together.

So I put together a YouTube video using narrated slides that walks through the process step by step — from adding noise during training, to denoising, text conditioning, and newer transformer-based models.

I’m still learning myself, so I’m sure there are places that can be improved or explained better.

If anyone here is willing to watch and give honest feedback, I’d genuinely appreciate it — especially from people with stronger technical understanding of diffusion models.

Constructive criticism is very welcome. If something is inaccurate, oversimplified, or unclear, please tell me so I can improve future videos.

I’ll place the link in the comments. Thank you.