The default values in these references are set well. you'll also find difference between fine tuning techniques in [2].

>discover arbitrary functions

Uh, no. Not even close. DL can approximate arbitrary functions on a bounded interval given enough data, parameters and compute.

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Ok it will try to share the resources here soon. I saved them somewhere yesterday

[1] J. Howard, “fast.ai - 1st Two Lessons of From Deep Learning Foundations to Stable Diffusion,” Oct. 19, 2022. https://www.fast.ai/posts/part2-2022-preview.html (accessed Feb. 20, 2023). [2] use_excalidraw, “Well-Researched Comparison of Training Techniques (Lora, Inversion, Dreambooth, Hypernetworks),” r/StableDiffusion, Jan. 15, 2023. www.reddit.com/r/StableDiffusion/comments/10cgxrx/wellresearched_comparison_of_training_techniques/ (accessed Feb. 20, 2023). [3] terrariyum, “Advanced advice for model training / fine-tuning and captioning,” r/StableDiffusion, Feb. 17, 2023. www.reddit.com/r/StableDiffusion/comments/114dxgl/advanced_advice_for_model_training_finetuning_and/ (accessed Feb. 20, 2023). [4] BarTraditional6305, “Alternative tools to fine tune stable diffusion models?,” r/StableDiffusion, Feb. 08, 2023. www.reddit.com/r/StableDiffusion/comments/10wqgok/alternative_tools_to_fine_tune_stable_diffusion/ (accessed Feb. 13, 2023). [5] Thick_Journalist_348, “LORA vs dream booth?,” r/StableDiffusion, Feb. 02, 2023. www.reddit.com/r/StableDiffusion/comments/10rihf7/lora_vs_dream_booth/ (accessed Feb. 13, 2023). [6] “AUTOMATIC1111/stable-diffusion-webui: Stable Diffusion web UI.” https://github.com/AUTOMATIC1111/stable-diffusion-webui (accessed Feb. 13, 2023). [7] AbdBarho, “Stable Diffusion WebUI Docker.” Feb. 13, 2023. Accessed: Feb. 13, 2023. [Online]. Available: https://github.com/AbdBarho/stable-diffusion-webui-docker [8] “stochasticai/x-stable-diffusion.” Stochastic, Feb. 13, 2023. Accessed: Feb. 13, 2023. [Online]. Available: https://github.com/stochasticai/x-stable-diffusion [9] N. W. Foong, “How to Fine-tune Stable Diffusion using Textual Inversion,” Medium, Oct. 24, 2022. https://towardsdatascience.com/how-to-fine-tune-stable-diffusion-using-textual-inversion-b995d7ecc095 (accessed Feb. 09, 2023). [10] N. W. Foong, “How to Fine-tune Stable Diffusion using Dreambooth,” Medium, Feb. 04, 2023. https://towardsdatascience.com/how-to-fine-tune-stable-diffusion-using-dreambooth-dfa6694524ae (accessed Feb. 09, 2023). [11] “The Annotated Diffusion Model.” https://huggingface.co/blog/annotated-diffusion (accessed Jan. 31, 2023). [12] J. Alammar, “The Illustrated Stable Diffusion.” https://jalammar.github.io/illustrated-stable-diffusion/ (accessed Jan. 31, 2023). [13] “Understanding Stable Diffusion from ‘Scratch.’” https://scholar.harvard.edu/binxuw/classes/machine-learning-scratch/materials/stable-diffusion-scratch (accessed Jan. 31, 2023).

Ok it will try to share the resources here soon. I saved them somewhere yesterday

>Today, science has explanations for many of those natural phenomena

Depends what you mean by explanations. You can always keep asking "why?" and at some point you won't be able to answer so you can just put god in there just like the early humans did. If you think what they did was reasonable then the same principle applies here, it's just that we have a deeper level of understanding

And the hardware.

Because every NN is just basically a big linear function… with a nonlinearity at the end.

Sorry, but why do we need another package? Can't you build on top of https://github.com/huggingface/peft ?

Reducing the information a system can represent requires it to learn generalized patterns rather than memorize events. In machine learning this will increase transfer some.

Thank you for the wonderful tool OP

Large linear models tend not to scale well to large datasets if the solution is not in the model class. Because of this lack of expressivity, linear models tend to do poorly on complex problems.

If you're interested in the math, learning curve theory might be a good place to start.

You could look at a YOLO tutorial, that's how I started back whenn YOLO was still new .

Maybe this repo will help you:

https://github.com/ultralytics/yolov5

But keep in mind that you will have to learn AI techniques to really be able to control the outcome of your model and that the training process can become unnecessarily expensive if you don't know when and how to optimize it.

Very Sick, these are the best ive seen. Thanks for making these. May i ask how you made them.

According to their paper, the LLM is doing task decomposition. You're able to give it high-level instructions like "go to the kitchen and make an omelette", and it breaks it down into actions like get eggs, get pan, get oil, put oil in pan, put eggs in pan, etc.

You could use something like this to give high-level instructions to a robot in plain English.

Not exactly but close enough?

hmm not sure, but I think if you don't exponentiate you cannot fit n targets into a d-dimensional space if n > d and you want there to exist a vector v for each target such that the outcome is a one-hot distribution (or 0 loss).

Basically, if you have 10 targets but only a 2-dimensional space you need to have enough non-linearity in the projection to your target space such that there exists a 2d vector which gives 0 loss for each target.

edit: MNIST only has 10 classes so you are probably fine. Furthermore, softmax of the dot product "care exponentially more" about the angle of the prediction vector than the scale. If you use norm, I'd think that you only care about angle which likely leads to different representations. The fact that those may improve performance highly depends how your model may rely on scale to learn certain predictions. Maybe in case of mnist, relying on scale worsens performance (e.g. if you want a wild guess, because it maybe makes "predictions more certain" simply if it has more pixels set to 1).

https://www.reddit.com/r/stablediffusion/wiki/tutorials/ ?

there's not a lot, and not a lot about machine learning tbh.

also, what i'm doing is fine-tuning? i've googled that as well and it's not totally clear to me what the differences would be. for fine tuning i did read (discord, stable tuner) that Class is not needed for doing that.

I mean this has two elements in it.

DL is not the only algorithm that works in scale for sure.

Haven't heard of GLMs being successfully used for NLP and CV in the recent time. And these are like the only things that would be described as large scale in ML. The statement is completely correct - even stuff like gradient boosting does not work at scale in that sense

I think you should see the fine tuning guide on r/Stable Diffusion sub reddit. There are a lot of guides there and you'll find huge information.

Well that good be an amazing post to read. How many times does it get math questions right but with an statistically significant number of samples. So that we can actually compare to the state of the art, such as galactica.

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I think I see where I crossed it over on a layman interpretation. My apologies there.