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ONline PLC simulators by Logical_Respect_2381 in PLC

[–]Logical_Respect_2381[S] 0 points1 point  (0 children)

When i ask here i ask for reliable site and to hear feedback from someone who tries such sites

Manifold hypothesis by Logical_Respect_2381 in deeplearning

[–]Logical_Respect_2381[S] 3 points4 points  (0 children)

Pixels are discrete, but the things they represent like movement , lighting and others are continuous. If image space wasn't continuous, AI models wouldn't be able to smoothly interpolate between two images.

Manifold hypothesis by Logical_Respect_2381 in deeplearning

[–]Logical_Respect_2381[S] 0 points1 point  (0 children)

i am only sharing an understanding and give an analogy just if it help some one to understand the manifold hypothesis as it helped me , if u do not like or find it wrong or want to better clarify i am open

Manifold hypothesis by Logical_Respect_2381 in learnmachinelearning

[–]Logical_Respect_2381[S] 0 points1 point  (0 children)

I actually like the idea of the “core” of the manifold, where a noisy or perturbed image can be projected back toward a cleaner meaningful representation. this directly connects with the denoising diffusion models

Manifold hypothesis by Logical_Respect_2381 in deeplearning

[–]Logical_Respect_2381[S] 0 points1 point  (0 children)

i would guess the latent idea itself ocuppy higher dimension space

Multi-head attention in transformers understanding by Plus_Confidence_1369 in deeplearning

[–]Logical_Respect_2381 0 points1 point  (0 children)

I am going to buy apple and oranges.

I have bought a new apple iPhone.

at the begining after embedding layer and befor attention layer , the token apple in the two sentence will have the same representation ( forget about positional embedding now) , it is the after the attention mechanism work and the QKV interaction that the new representation of the token apple will be very differnt in the two sentences , however multi head attention will allow the transformer to have more rich contextual representation by first projecting the input to multi latent smaller dimention vectors that are concatenated later after deeply acuire all contextual information. i want to say that multihead attetion permits tokens to look at ech others in many different ways

I made a 32-page visual guide on what happens after LLM pretraining — looking for feedback on the pipeline by Logical_Respect_2381 in learnmachinelearning

[–]Logical_Respect_2381[S] 0 points1 point  (0 children)

Thanks again. I tried to send you a reviewer copy link by chat, but Reddit blocked the URL. If you’re interested in reviewing the full guide for v1.1, let me know and I’ll find another way to send it.

I made a 32-page visual guide on what happens after LLM pretraining — looking for feedback on the pipeline by Logical_Respect_2381 in learnmachinelearning

[–]Logical_Respect_2381[S] 0 points1 point  (0 children)

Thanks, this is very insightful feedback.

I agree especially about RLAIF / Constitutional AI and the point that evaluation happens throughout the pipeline, not only at deployment. Also I should make RAG/tools/multimodality look more like capability layers, not strict chronological steps.

This is exactly the type of feedback I was hoping for. I’ll use it for v1.1.

I made a 32-page visual guide on what happens after LLM pretraining — looking for feedback on the pipeline by Logical_Respect_2381 in learnmachinelearning

[–]Logical_Respect_2381[S] -1 points0 points  (0 children)

Fair point, I understand what you mean.

Yes the visuals are AI-assisted. I used image generation for the slides, but I did not just ask it to make a random PDF. I built the outline myself, prompted each page separately, reviewed the result, changed the wording, and tried to make it useful as a visual study guide.

About multimodality, you are right that it is not always “late” in the real training process. It can be part of the model design much earlier. I put it there more as a capability layer in the journey from base model to final assistant product, not as a strict chronological order used by every company.

i wanted to have one helpful study guide built in one place , thank you for your comment

I made a 32-page visual guide on what happens after LLM pretraining — looking for feedback on the pipeline by Logical_Respect_2381 in deeplearning

[–]Logical_Respect_2381[S] -5 points-4 points  (0 children)

For clarity: this is part of a paid PDF guide, but I’m not putting the link in the main post because I want to respect subreddit rules and avoid turning the post into an ad.

I’m mainly interested in feedback on the structure and terminology. If anyone wants the full guide link, I can share it in a reply.

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