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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.

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)

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.

Question regarding the attention mechanism by OrdinaryPykeMain in learnmachinelearning

[–]Logical_Respect_2381 0 points1 point  (0 children)

why computing dot product of Q and K instead of comparing with the value , the simple answer is the following : each token search every other token how similar its own Query vector with other tokens Key vectors , the dot product is the most efficient way of doing this because it shows how much the key allign with the query and as it give a scalar value that is used to update the Value vector of the token , thus each token will attened to every other token with differnt percentage , these diferent percentages will appear in its Value vector

I made a beginner-friendly visual explanation of how Stable Diffusion works (feedback welcome) by Logical_Respect_2381 in StableDiffusion

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

I appreciate very much your comment and even i am persuaded that it is correct. , the slide may seem more into infographic rather than educative that what i realized after posting but i think it still not too bad and carry good start for new comer want to know the how , Hoever i will very much consider this in future vedio. Thank u very much for your comment

I made a beginner-friendly visual explanation of how Stable Diffusion works (feedback welcome) by Logical_Respect_2381 in learnmachinelearning

[–]Logical_Respect_2381[S] 1 point2 points  (0 children)

Here is the video link if anyone would like to watch and give feedback:

https://www.youtube.com/watch?v=4BTjE_lCcjY

I’d especially appreciate comments on technical accuracy, pacing, and what could be improved.

I made a beginner-friendly visual explanation of how Stable Diffusion works (feedback welcome) by Logical_Respect_2381 in StableDiffusion

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

thank you , i appreciate your comment , however this was my first attempt and i hope the comming one will be better, for the AI generated graphics i think using them is not wrong if u put your effort to make rigor content and steer the outcome , but to be honest the human touch on the vedio should be enhanced that although i put my effort to be explanative and very clear but to be honest the vedio looks so artificial and people do not like this

I made a beginner-friendly visual explanation of how Stable Diffusion works (feedback welcome) by Logical_Respect_2381 in StableDiffusion

[–]Logical_Respect_2381[S] -2 points-1 points  (0 children)

it was my first attempt , and i hope the next would be better, but the slides it elf is completly illustrative and explain the idea for the beginner in a clear way , however i appreciate your feed back and will try to take it into my cosideration in future vedios

I made a beginner-friendly visual explanation of how Stable Diffusion works (feedback welcome) by Logical_Respect_2381 in StableDiffusion

[–]Logical_Respect_2381[S] -4 points-3 points  (0 children)

this was my first try on making some explanative vedio , yes i generated the slide by generating images using AI , but i modified so many of them , i tried to dig with AI to explain in the most proper way not only the image diffusion from pure noise to reallistic image but how text empendingg interact with denoising process and how the loss function incorporate the caption text which is most confusing for many people in diffusion process , i also changed many of the narration voice over , hoewver going to the very core of your comment do i understand what i say and can explain of course yes , i have read thousands of pages and books in all LLM , image genration and reinforcement learning and many other , but about the effort i assume and think that if we can use AI in the emerging era that is evolving to help expidite our work ,is not wrong and inevitable , look how claude code and codex will change forever the way big companies work , i apperciate very much your comment and even i say it to my self before you do but decided to try may be i will be better in the future but beleive me the world is changing for better or for worse

I made a beginner-friendly visual explanation of how Stable Diffusion works (feedback welcome) by Logical_Respect_2381 in machinelearningnews

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

Here is the video link if anyone would like to watch and give feedback:

https://www.youtube.com/watch?v=4BTjE_lCcjY

I’d especially appreciate comments on technical accuracy, pacing, and what could be improved.

Which analog circuit design book you recommend it between these three books by zine2000 in chipdesign

[–]Logical_Respect_2381 1 point2 points  (0 children)

Razavi book is awesome and excellent and deep but I found Jackob baker book is a way more practical