playground
clip-interrogator
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playground | clip-interrogator | |
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16 | 27 | |
11,674 | 2,471 | |
1.1% | - | |
0.0 | 4.8 | |
3 months ago | 3 months ago | |
TypeScript | Python | |
Apache License 2.0 | MIT License |
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playground
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Why do tree-based models still outperform deep learning on tabular data? (2022)
Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good.
For example, take the circle dataset here: https://playground.tensorflow.org
That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by radius would do the trick. Now try doing that in 1000 dimensions. Sometimes you can, sometimes you can't or do want to bother.
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Introduction to TensorFlow for Deep Learning
For visualisation and some fun: http://playground.tensorflow.org/
- TensorFlow Playground – Tinker with a NN in the Browser
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Visualization of Common Algorithms
https://seeing-theory.brown.edu/
https://www.3blue1brown.com/
https://playground.tensorflow.org/
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Stanford A.I. Courses
There’s an interactive neural network you can train here, which can give some intuition on wider vs larger networks:
https://mlu-explain.github.io/neural-networks/
See also here:
http://playground.tensorflow.org/
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Let's revolutionize the CPU together!
This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a “classifier”, to try to classify data points into whatever category they best belong in.
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Curious about Inputs for neural network
I don’t know much experimenting you’ve done, but many repeated small scale experiments might give you a better intuition at least. I highly recommend this online tool for playing with different environmental variables, even if you’re comfortable coding up your own experiments: http://playground.tensorflow.org
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Intel Announces Aurora genAI, Generative AI Model With 1 Trillion Parameters
Even if you can’t code, play around with this tool: https://playground.tensorflow.org — you can adjust the shape of the NN and watch how well it classifies the data. Model size obviously matters.
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Where have all the hackers gone?
I don't think so. You can easily play around in the browser, using Javascript, or on https://processing.org/, https://playground.tensorflow.org/, https://scratch.mit.edu/, etc.
If anything the problem is that today's kids have too many options. And sure, some are commercial.
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[Discussion] Questions about linear regression, polynomial features and multilayer NN.
Well there is no point of using a multilayer linear neural network, because a cascade of linear transformations can be reduced to a single linear transformation. So you can only approximate linear functions. However if you have prior knowledge about the non linearity of your data lets say you know that it is a linear combination of polynomials up to certain degree, you can expand your input space by explicitly making non linear transformation. For instance a 1D linear regression can be modeled by 2 input neurons and 1 output neuron where the activation of the output is the identity. The input neuron x0 will take a constant input namely 1 and the second input neuron x1 will takes your data x. The output neuron will be y=w_0 * 1+w_1 *x which is equal to y=w_0 +w_1 * x. Let us say that your data follows a polynomial form, the idea is to add input neurons and expand your input to for instance X=[1 x x2] in this case you have 3 input neurons where the third is an explict non linear form of the input so y=w_0 + w_1 x +w_2 x2. The general idea is to find a space where the problem becomes linear. In real life example these spaces are non trivial the power of neural network is that they can find by optimization such space without explicitly encoding these non linearities. Try playing around with https://playground.tensorflow.org/ you can get an intuition about your question.
clip-interrogator
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AI Horde’s AGPL3 hordelib receives DMCA take-down from hlky
It's image -> words, the inverse of stable diffusion.
see: https://github.com/pharmapsychotic/clip-interrogator
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What are the "fastest" image classifiers I can use?
I have been using this on a CPU https://github.com/pharmapsychotic/clip-interrogator, I tried a lot of pre-trained models combinations, all are slow.
- -New Monthly Event!-
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I keep trying to recreate this scene as a painting. But the AI doesn't get it. How do I describe that the man is reaching behind to stab a lion in the head, as the lion has pounced and is biting the rear of the horse. The AI always redraws this without the lion or not how it is shown here.
I'm addition to controlnet, try the clip interrogator to see how clip would describe the image and then use that language in your prompt. You can try the whole image or cropped portions. There is a colab available if you don't want to run it locally.
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For Lora training, isn’t there a good AI that discribes the pictures you want to use for training?
In my current process, I use CLIP Interrogator to produce a high level caption and wd14 tagger for more granular booru tags. Typically in that order, because you can append the results from the latter to the former. Both tools perform with greater accuracy than the standard interrogators in img2img and give you more flexibility and features as well. You still have to do some manual adjustments, but I generally prefer this process over starting from scratch.
- Midjourney Image2text
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Tech pioneers call for six-month pause of "out-of-control" AI development
If you are interested in this, definitely see if you can get some of the OSS models running and get a feel for how to interrogate them. Maybe see if you can get some mileage out of the CLIP-Interrogator
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ChatGPT 3.5 vs 4 & Stable Diffusion
Next, I used the lists of artists, flavors, mediums, movements, and negatives that are used for the clip-interrogator and pasted these in the chat and told the bot to categorize them accordingly. As you can only paste up to certain characters in single message (4-5K in 3.5 and 6-8K in 4).
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Any idea of what type of prompt has been used to make this?
Here’s the specific one I’m using (runs in browser)
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CLIP Interrogator 2 locally
I really enjoy using the CLIP Interrogator on huggingspaces, but it is often super slow and sometimes straight up breaks. Now it is possible to locally install it, https://github.com/pharmapsychotic/clip-interrogator but I don't know if its viable to run on a laptop with 6gb videocard anyway.
What are some alternatives?
dspy - DSPy: The framework for programming—not prompting—foundation models
stable-diffusion-webui-wd14-tagger - Labeling extension for Automatic1111's Web UI
nvim-treesitter - Nvim Treesitter configurations and abstraction layer
laion-datasets - Description and pointers of laion datasets
pyllama - LLaMA: Open and Efficient Foundation Language Models
dalle-2-preview
lake.nvim - A simplified ocean color scheme with treesitter support
stable-diffusion-artists - Curated list of artists for Stable Diffusion prompts
developer - the first library to let you embed a developer agent in your own app!
hordelib - A wrapper around ComfyUI to allow use by the AI Horde. [UnavailableForLegalReasons - Repository access blocked]
machine-learning-specialization-andrew-ng - A collection of notes and implementations of machine learning algorithms from Andrew Ng's machine learning specialization.
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps