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Merge pull request #1021 from nunofachada/patch-1

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Add several Clugen implementations + TextCL
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Joseph Misiti 2025-02-13 08:50:59 -05:00 committed by GitHub
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@ -726,6 +726,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
* [Knet](https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework.
* [Flux](https://fluxml.ai/) - Relax! Flux is the ML library that doesn't make you tensor
* [MLJ](https://github.com/alan-turing-institute/MLJ.jl) - A Julia machine learning framework.
* [CluGen](https://github.com/clugen/CluGen.jl/) - Multidimensional cluster generation in Julia.
<a name="julia-natural-language-processing"></a>
#### Natural Language Processing
@ -880,7 +881,7 @@ on MNIST digits[DEEP LEARNING].
* [Optunity](https://optunity.readthedocs.io/en/latest/) - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly with MATLAB.
* [MXNet](https://github.com/apache/incubator-mxnet/) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [Machine Learning in MatLab/Octave](https://github.com/trekhleb/machine-learning-octave) - Examples of popular machine learning algorithms (neural networks, linear/logistic regressions, K-Means, etc.) with code examples and mathematics behind them being explained.
* [MOCluGen](https://github.com/clugen/MOCluGen/) - Multidimensional cluster generation in MATLAB/Octave.
<a name="matlab-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization
@ -1108,6 +1109,7 @@ be
* [Haystack](https://github.com/deepset-ai/haystack) - A framework for building industrial-strength applications with Transformer models and LLMs.
* [CometLLM](https://github.com/comet-ml/comet-llm) - Track, log, visualize and evaluate your LLM prompts and prompt chains.
* [Transformers](https://github.com/huggingface/transformers) - A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
* [TextCL](https://github.com/alinapetukhova/textcl) - Text preprocessing package for use in NLP tasks.
<a name="python-general-purpose-machine-learning"></a>
#### General-Purpose Machine Learning
@ -1280,6 +1282,7 @@ be
* [CometML](https://github.com/comet-ml/comet-examples): The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
* [Okrolearn](https://github.com/Okerew/okrolearn): A python machine learning library created to combine powefull data analasys feautures with tensors and machine learning components, while mantaining support for other libraries.
* [Opik](https://github.com/comet-ml/opik): Evaluate, trace, test, and ship LLM applications across your dev and production lifecycles.
* [pyclugen](https://github.com/clugen/pyclugen) - Multidimensional cluster generation in Python.
<a name="python-data-analysis--data-visualization"></a>
#### Data Analysis / Data Visualization
@ -1645,6 +1648,7 @@ be
* [igraph](https://igraph.org/r/) - binding to igraph library - General purpose graph library.
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
* [TDSP-Utilities](https://github.com/Azure/Azure-TDSP-Utilities) - Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modelling and Reporting (AMR).
* [clugenr](https://github.com/clugen/clugenr/) - Multidimensional cluster generation in R.
<a name="r-data-analysis--data-visualization"></a>
#### Data Manipulation | Data Analysis | Data Visualization