I guess it depends on what your use-case is. How about https://github.com/HarisIqbal88/PlotNeuralNet

How do you envision searching?

maybe something like scraperapi but with some kind of Dsl one could send as post payload.

but als a Problem is that you often need a scraped result as input for another request

Unfortunately a lot of ML is just trial and error

Are there any examples of learning higher-rank tensors in machine learning?

Possibly, yes, I would need to check. I recently built parsing services for tiktok, and it was super annoying to deal with.

There was a court ruling a year or two ago that concluded that scraping public linkedin profiles is legal :) LN obviously still doesn't want you to scrape their data, so building scrapers for it is extra tedious because you need to navigate their blocking.

There is an old thread on reddit: https://www.reddit.com/r/MachineLearning/comments/l1z8cr/d_best_way_to_draw_neural_network_diagrams/

Personally, I like http://alexlenail.me/NN-SVG/LeNet.html

Thanks! this was an interesting read.

What are the situations that the bias for the hierarchy is not helpful?

I work in NLP. Work mainly consists of fine tuning NLP models. With the rise of LLMs I'm seeing a lot of my work becoming Prompt engineering. I'm happy to pick up the new skill but I'd like to know what avenues I have to upskill beyond being a prompt engineer without a PhD. Feels like all the learning I did on model architectures etc is going to waste. There are still a few projects that need me to fine tune a model for text classification etc but as LLMs get better I suspect I need better skills to go beyond becoming a prompt engineer. For anyone else in NLP who doesn't have a PhD and doesn't have any experience building model architectures/training from scratch etc, how are all of you trying to up skill in these times? EDIT: Worded the question to ask only people who don't have a PhD, I would actually like to know everyone's perspective on this.

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I recognized the ResNet bottleneck!

Very cool, you did a great job there.

Also, some of it might give you legal trouble if you f e make a public crawler for linkedin

Man i feel so behind that i didnt even know LoRA.

building and maintaining scrapers is tedious! I would also like some better solution. the idea is not bad, just maybe difficult to solve.

The only thing worst than those posts are these posts.

Obviously there's going to be noobs here who don't understand anything about ML. If you don't want to engage with them, then just don't.

If you're such a hardass you can't put up being around some noobs, just sit in your basement and read ML papers all day

No. Even though the high-level class is named Translator, it can be used to run any tasks that would work using T5ForConditionalGeneration in the transformers library.

For flan-t5 does this only work for a Translation task?

My goal is to deploy an ML model which can perform price prediction on an exchange traded fund (ETF) - which is essentially an aggregation of stocks. A very popular ETF is the S&P 500 (which is not actually the ETF I want to predict on. The one I want to predict on only has about 30 stocks).

Can an ML model trained/tested on individual stocks which are *not* in the ETF a valid way of building a model which can accomplish price prediction on an ETF?

> It does memorize short snippets in some cases (especially when a snippet is repeated many times in training data)

And, to be fair, how can it not? How many different ways can you write a simple for loop to print some objects, or match a regex, call an API, and so on?

Might want to look into something like https://spacy.io/

That's true for all of the models. I don't really need anything cool though, all I need is a solid model that can perform simple tasks like text classification or NER well.

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What are you trying to do? Most of the cool features of language models only emerge at much larger scales.