The Best LDA.js Alternatives in 2026

Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.

Prediction with gradient boosting regression tree models is implemented in leaves, a Go software library.

Netron displays neural network, deep learning, and machine learning models across formats such as ONNX, TensorFlow, and PyTorch.

Weights & Biases supports model training, fine tuning, and management from experimentation through production.

Python developers can use gensim for topic modeling and related natural language processing tasks.

Python developers can use Zipline’s event-driven library to backtest algorithmic trading strategies.

Segment Chinese text in PHP applications with jieba-php, a library for Chinese word tokenization.

The fastai deep learning library includes documentation and setup guidance, including instructions for use with Google Colab.

The Geni project provides a Clojure dataframe library that runs on Apache Spark.

Automatic machine learning, hyperparameter tuning, and pipeline abstractions are provided by the Neuraxle software library.

crystal-fann provides Crystal language bindings for the FANN artificial neural network library.

The Pattern Recognition Toolbox provides MATLAB tools for pattern recognition and machine learning.

Browse large JSONL, Parquet, and CSV datasets in VS Code with the Bread Dataset Viewer extension.

jSciPy provides Java scientific computing tools for signal processing, FFT, filtering, power spectral density, and DCT.

ONNX-Scala provides an API and backend for deep and classical machine learning in Scala.

Opyrator turns machine learning code into microservices with a web API and interactive interface.

torchaudio provides data manipulation and transformation tools for audio signal processing with PyTorch.

The anneal project is a Go machine learning compiler with graph rewriting, autodiff, and a WebGPU backend.

Sparkling Water brings H2O machine learning functionality into an Apache Spark cluster.

For Go developers, GoLearn provides a collection of tools for machine learning.

Mars provides tensor-based tools for large-scale computation with NumPy, Pandas, and scikit-learn.

Tesseract provides an open-source OCR engine for extracting text from images, with its main repository hosted on GitHub.

Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.

Browse a gallery of interesting Jupyter notebooks on this project wiki, with examples from notebook users.