Weka
Deep Java Library (DJL)
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| Weka | Deep Java Library (DJL) | |
|---|---|---|
| - | 13 | |
| 302 | 3,830 | |
| - | 1.9% | |
| 0.0 | 9.4 | |
| almost 5 years ago | 6 days ago | |
| PostScript | Java | |
| - | Apache License 2.0 |
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Weka
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Tracking mentions began in Dec 2020.
Deep Java Library (DJL)
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Is deeplearning4j a good choice?
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.
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Just want to vent a bit
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.
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Best way to combine Python and Java?
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).
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Anybody here using Java for machine learning?
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.
- Good document classification library in Java
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2021-09 - Plans & Hopes for Clojure Data Science
Here is link number 1 - Previous text "DJL"
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[D] Java vs Python for Machine learning
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.
- Does Java has similar project like this one in C#? (ml, data)
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If it gets better w age, will java become compatible for machine learning and data science?
I think DJL also use use it for their tutorials - https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html.
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Machine learning on JVM
AWS Deep Learning more deep learning.
What are some alternatives?
Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Apache Hadoop - Apache Hadoop
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
Smile - Statistical Machine Intelligence & Learning Engine
Tribuo - Tribuo - A Java machine learning library
H2O - Sparkling Water provides H2O functionality inside Spark cluster
CoreNLP - CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
Apache Mahout - Mirror of Apache Mahout
Apache Flink - Apache Flink