Recent comments in /f/MachineLearning

trnka t1_j6583q3 wrote

Reply to comment by InsidiousApe in [D] Simple Questions Thread by AutoModerator

If you're ingesting from an API, typically the limiting factor is the number of API calls or network round trips. So if there's a "search" API or anything similar that returns paginated data that'll speed it up a LOT.

If you need to traverse the API to crawl data, that'll slow it down a lot. Like say if there's a "game" endpoint, a "player" endpoint, a "map" endpoint, etc.

If you're working with image data, fetching the images is usually a separate step that can be slow.

After that, it you can fit it in RAM you're good. If you can fit it on one disk, there are decent libraries with each ML framework to efficiently load from disk in batches, and you can probably optimize the disk loading too.

----

What you're describing is usually called exploratory data analysis but it depends on the general direction you want to go in. If you're trying to identify people with thyroid cancer earlier, for example, you might want to compare the data of recently-diagnosed people to similar people that have been tested and found not to have thyroid cancer. Personally, in that situation I like to just train a logistic regression model to predict that from various patient properties then check if it's predictive on a held-out data sample. If it's predictive I'll then look at the coefficients of the features to understand what's going on, then work to improve the features.

Another simple thing you can do, if the data is small enough and tabular rather than text/image/video/audio is to load it up in Pandas and run .corr then check correlations with the column you care about (has_thyroid_cancer).

Hope this helps! Happy to follow up too.

2

currentscurrents t1_j657n2z wrote

Reply to comment by blimpyway in [R] The Predictive Forward-Forward Algorithm by radi-cho

>The so called NPUs. Which are simplified GPUs optimized only for inference (forward passes). Such an algorithm would enable them to learn using only forward passes, hence without requiring backpropagation.

More importantly, you could build even simpler chips that physically implement a neural network out of analog circuits instead of emulating one with digital math.

This would use orders of magnitude less power, and also let you fit a larger network on the same amount of die space.

1

Blutorangensaft OP t1_j654qyd wrote

Reply to comment by jackilion in [D] Quantitative measure for smoothness of NLP autoencoder latent space by Blutorangensaft

Thank you for the reference, it looks very promising. I've heard of ways to smooth the latent space through Lipschitz regularisation, but then got disappointed again when I read "ah well it's just layer normalisation". So many things in ML come in a different appearance and actually mean the same thing once you implement them.

1

mil24havoc t1_j64s1uy wrote

Reply to comment by red_dragon in [P] Using algorithms or models from papers for commercial use by romantimm25

The weights are part of the model, not the algorithm. Whether these can be copyrighted is (a) unclear and (b) should have no bearing on the status of the algorithm itself.

Edit: The output of an algorithm has been ruled by courts to not be copyrightable. A Transformer is, itself, the "output" of an algorithm (e.g., SGD). Therefore, IMHO (IANAL), a Transformer cannot be copyrighted. We'll see if the judges who start taking these cases are savvy enough to rule correctly. Similarly, recipes cannot be copyrighted and they're quite similar to algorithms.

7