quine-relay VS Deep Java Library (DJL)

If there were more languages, then it'd be `console.log("System.out.println({python_source})")`, etc. The problem then becomes quoting and escaping inner quotes. I managed to avoid the problem by using both single and double quotes, and relying on Python's `repr` also giving valid JS strings, but if I had to add one more language I'd have problems.

I still think the Quine Relay is a tour de force, but for different reasons. It's not 128 quines in different languages, but an incredibly robust system for quoting and escaping strings in 128 different languages.

[1] https://github.com/mame/quine-relay/blob/master/src/code-gen...

One of the most impressive works I've seen: https://github.com/mame/quine-relay

"Quine" is a type of program, when executed, will output itself (it's actually it's source code). It's very hard to write one. And this guy wrote a loop quine. It supposed to work like this: * A program in language A output a program in language B * The program in language B output a program in language C * The program in language C output the same program in language A which we started with

Or a Quine relay.

Why use a couple of languages, when you can use 128 of them simultaneously? https://github.com/mame/quine-relay

Quine relay [1] is to this day the most "I will never understand this" brainfuck project I have ever seen.

[1] https://github.com/mame/quine-relay

Go look at a programming quine and tell me it isn't art.

It seems to have been picked up by Eclipse and there is also Oracle Labs' Tribuo and Deep Java Library. All seem active, but I don't know much about any of them. I agree it's probably best to follow the community and use a more popular tool like PyTorch.

Although it may be a bit more work, you can do both machine learning and AI in Java. If you are doing deep learning, you can use DeepJavaLibrary (I do work on this one at Amazon). If you are looking for other ML algorithms, I have seen Smile, Tribuo, or some around Spark.

Image preprocessing I know less about, but tokenization is something I've dealt with a bunch. There are a few options, either push the tokenizer into the ONNX model and use MS's ONNX Runtime extensions (we've used this when working with sentencepiece tokenizers), port the tokenizer entirely to Java (we did this for BERT), or use a sentencepiece or HF tokenizers wrapper directly (e.g. Amazon's DJL did this - HF, sentencepiece).

https://djl.ai/ seems very promising. I've played around with it quite a bit, not in real production though. It's a very well documented (https://d2l.djl.ai/) and active project, with Amazon working on it.

Here is link number 1 - Previous text "DJL"

To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.

I think DJL also use use it for their tutorials - https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html.

AWS Deep Learning more deep learning.