Displaying 1 to 4 from 4 results

igel - a delightful machine learning tool that allows you to train, test, and use models without writing code

  •    Python

The goal of the project is to provide machine learning for everyone, both technical and non-technical users. I needed a tool sometimes, which I can use to fast create a machine learning prototype. Whether to build some proof of concept or create a fast draft model to prove a point. I find myself often stuck at writing boilerplate code and/or thinking too much of how to start this.

jlearn - Machine Learning Library, written in J

  •    J

WIP Machine learning library, written in J. Various algorithm implementations, including MLPClassifiers, MLPRegressors, Mixture Models, K-Means, KNN, RBF-Network, Self-organizing Maps. Models can be serialized to text files, with a mixture of text and binary packing. The size of the serialized file depends on the size of the model, but will probably range from 10 MB and upwards for NN models (including convnets and rec-nets).

pytorch-kaldi - pytorch-kaldi is a project for developing state-of-the-art DNN/RNN hybrid speech recognition systems

  •    Perl

pytorch-kaldi is a public repository for developing state-of-the-art DNN/RNN hybrid speech recognition systems. The DNN part is managed by pytorch, while feature extraction, label computation, and decoding are performed with the kaldi toolkit. The provided solution is designed for large-scale speech recognition experiments on both standard machines and HPC clusters.

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