hello-algorithm VS Deep Java Library (DJL)

you can try the premium ones on https://www.lintcode.com/ unless it's changed since the last time I prepped

Just FYI, if you have the name of the problem, you can find it on : - https://leetcode.ca/all/problems.html - https://www.lintcode.com/

geekxh/hello-algorithm (Java): 🌍 东半球最酷的学习项目 | 1、我写的三十万字算法图解 2、千本开源电子书 3、100 张思维导图 4、100 篇大厂面经 5、30 个学习专题 🚀 🚀 🚀 右上角点个 star，加入我们万人学习群！English Supported！

# install release version of djl-converter pip install https://publish.djl.ai/djl_converter/djl_converter-0.30.0-py3-none-any.whl # install from djl master branch pip install "git+https://github.com/deepjavalibrary/djl.git#subdirectory=extensions/tokenizers/src/main/python" # install djl-convert from local djl repo git clone https://github.com/deepjavalibrary/djl.git cd djl/extensions/tokenizers/src/main/python python3 -m pip install -e . # Add djl-convert to PATH (if installed locally or not globally available) export PATH="$HOME/.local/bin:$PATH" # install optimum if you want to convert to OnnxRuntime pip install optimum # convert a single model to TorchScript, Onnxruntime or Rust djl-convert --help # import models as DJL Model Zoo djl-import --help

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.