You could try to ask your University if they have a computer cluster/server for these things or alternatively ask if they would reimburse the cost for colab resources

Watch Karpathy's videos about it and you good to go!

Shameful plug, but I didn't want copy and format links on mobile.

https://github.com/veb-101/Neural-Networks-from-scratch/tree/master/Multi-Layer-Perceptron

In the readme I've listed the links that I used for learning and implementing neural networks from scratch.

Write all the equations out one paper, then do one forward and backward pass on paper as well with a simple mlp. I believe bias can be easily incorporated using an extra 1 in the input and using an extra weight as the bias, so it’s updated the same as any other weight. Also learn the basics of matrix multiplication.

Cool, this seems interesting. Do you know what CPUs they are ?

That would be funny infrastructure startup that doesn't know anything about infrastructure LOL we're a robotics company :)

Deep learning is much different from typical programming topics in that it is composed of a large amount of mathematical and complex theoretical concepts that are not avoidable using a library. Getting the code to run is relatively easy, and the choice of library has mostly to do with deployment goals and utilizing existing implementations. When things aren't working, theres not really a compiler error as to what's wrong with the model/data pipeline, and deep theoretical knowledge comes into play.

I very much enjoyed The Kaggle Book, which shares many useful insights from Kaggle grandmasters from their competition experience. Many of them are also active on Twitter where they share more practical tips.

Deep Learning by Goodfellow really gave me some good insights and tips for training a model, also covering the mathematical background around the ideas.

Would a Siamese network work here?

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Train a GAN on the images of class A. The generator will draw samples from the distribution outlined by the images in class A. The discriminator measures the distance between given sample and this distribution. So once you finish training on class A, the critic will tell you whether or not a given image belongs to class A.

An alternative approach is to do self-supervised representation learning (like BYOL) and compare the projection distance between a pair of A and B images.

Interesting - so it’s not only about the practical details of using those libraries but also contains general nuggets of info? I use PyTorch.

I always use the deeplearningbook from Goodfellow and Mathematics for machine learning as reference. Although old, the first book is definitely worth the effort.

You're a bit stuck, surely..? As far as the model is concerned, there is no class 'B'. Would mashing up images in 'A' be allowed? Random images? Noise?

come back to this planet in 10 years and everybody will be screaming 1s and 0s at each other.

Hands on machine learning with tensorflow and scikit learn

Here's an example of a CNN where the first layer is set to a fixed front-end while the rest of the network is learned (in this case the fixed front-end is a more neuroscientific model of early visual cortex V1): paper, github

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Look into OOD (Out of distribution) sample detection. If you go down the auto-encoder route then this paper can give you some pointers: Detecting Out-of-distribution Samples via Variational Auto-encoder with Reliable Uncertainty Estimation

Please note that OOD sample detection is an open problem and active research topic.

No. I was just using pandas (cpu) for simple quick regex and removing and replacing text rows. It was just for a hobby project. The data was scraped from Midjourney and Stable diffusion discord so there were millions of rows of duplicate prompts and poor quality prompts which I had pandas delete and in the end the number of unique rows with more than 50 characters amounted to about 700k which was then used to train gpt-neo 125m.

I didn't know about cudf. Thanks 😅

This is known as one-class learning or one-class classification. You could try obtaining "background class" images (images similar to yours in resolution, overall brightness, ...) and training an ordinary classifier on the combination of the two. Obviously, the background class images cannot contain food, but searches for things unrelated to food ("nail", "dancer", "floor", "statue", ...) followed up by quick visual inspection should serve.

>. For example, the largest Parque file I've cleaned in pandas was about 7 million rows and about 10gb in size of just text. It can run queries through it in a few seconds.

Using rapids? Like cudf?

I've only starting learning DL a month ago so mostly have been doing simple ANN. But inferencing larger param NLP models, GANS, Diffusion models, etc is fine. It's no desktop 3090s or enterprise grade GPU but for a laptop it's by far the best on the market. For example, the largest Parque file I've cleaned in pandas was about 7 million rows and about 10gb in size of just text. It can run queries through it in a few seconds.

Guess it depends on what kind of data science or dl you're looking to do. The 3080s probably won't be able to fine tune something like BLOOM model but can fine tune stable diffusion models with enough optimizations.

For modeling in blender or procedural generation in something like Houdini, I haven't had issues. I've made procedurally generated 20km height maps in Houdini to export to Unreal Engine and was not a problem.

mining rigs are for mining not dl