Deep Java Library (DJL) VS Pytorch

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Regarding Tensorflow: As far as I understand, it is accessible through DJL, which has a Clojure wrapper (work in progress): clj-djl. (But I haven't tried it.)

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.

awslabs/djl (Java): An Engine-Agnostic Deep Learning Framework in Java

Issue tracker

Release notes are still pending.

Maybe related: https://github.com/pytorch/pytorch/issues/12873

Let's dive into the main topic of this article, we are going to train a transformer model for Swahili news classification, Since transformers are large to make the task simple we need to select a wrapper to work with, if you are good with PyTorch you can use PyTorch Lightning a wrapper for high-performance AI research, to wrap the transformers but today lets go with ktrain from Tensorflow Python Library.

The second option is to train your own model using machine learning frameworks like Tensorflow and Pytorch.

The goal of this approach is to replace the intermediate steps with one algorithm. The deep learning approach has achieved state-of-the-art results in speech transcription tasks and is replacing the traditional methods used in ASR. It is also simpler because there are fewer steps involved and does not require as much expertise. The implementation of this approach requires a knowledge understanding of deep learning tools such as PyTorch, Tensorflow, DeepSpeech, etc.