Machine Learning Software

Popular software on machine learning and deep learning

Why popular: Provides a consistent interface to various machine learning algorithms and simplifies the process of building, training, and evaluating models.
Important functionalities: Data preprocessing, feature selection, model training, hyperparameter tuning, and performance evaluation.

Why popular: Implements a popular ensemble learning method that is easy to use and has good performance on a wide range of problems.
Important functionalities: Classification, regression, variable importance estimation, and proximity analysis using random forests.
Authors: Leo Breiman, Adele Cutler, Andy Liaw, Matthew Wiener
Citation: Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News, 2(3), 18-22. URL: https://cran.r-project.org/doc/Rnews/Rnews_2002-3.pdf.

Why popular: Implements an efficient and powerful gradient boosting algorithm that has demonstrated strong performance on various machine learning tasks and has been widely adopted in data science competitions.
Important functionalities: Gradient boosting for classification and regression, handling of missing data, parallel and distributed computing support.
Author: Tianqi Chen
Citation: Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. URL: http://dx.doi.org/10.1145/2939672.2939785.

Why popular: Implements generalized linear models with regularization (LASSO and Ridge regression), which helps prevent overfitting and improve model generalization.
Important functionalities: Linear regression, logistic regression, and Cox regression with LASSO and Ridge regularization, cross-validation for hyperparameter tuning.
Authors: Jerome Friedman, Trevor Hastie, Rob Tibshirani
Citation: Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1-22. URL: http://www.jstatsoft.org/v33/i01/.

Why popular: Provides an R interface to the Keras deep learning library, enabling users to define and train deep learning models in R using TensorFlow as the backend.
Important functionalities: Support for various types of neural networks (feedforward, convolutional, and recurrent networks), pre-processing, data augmentation, and real-time visualization of training progress.
Authors: JJ Allaire, François Chollet
Citation: Allaire, J.J., Chollet, F. (2019). keras: R Interface to 'Keras'. R package version 2.2.4.1. URL: https://CRAN.R-project.org/package=keras.

Why popular: Provides a simple and consistent interface to a wide range of machine learning algorithms, making it easy to build, train, and evaluate models.
Important functionalities: Preprocessing, feature selection, dimensionality reduction, model training, hyperparameter tuning, and performance evaluation for various classification, regression, and clustering algorithms.
Authors: Scikit-learn developers
Citation: Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825-2830. URL: https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html.

Why popular: Developed by Google, TensorFlow is a powerful and flexible open-source library for numerical computation and machine learning, with a particular focus on deep learning.
Important functionalities: Support for various neural network architectures, distributed computing, and GPU acceleration, as well as an extensive ecosystem of tools and libraries.

Google Brain Team
Abadi, Martín, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado et al. "Tensorflow: Large-scale machine learning on heterogeneous distributed systems." arXiv preprint arXiv:1603.04467 (2016). (Cited +25,000 times)

Why popular: A high-level deep learning library built on top of TensorFlow, Keras provides an easy-to-use interface for defining and training deep learning models.
Important functionalities: Support for various types of neural networks (feedforward, convolutional, and recurrent networks), pre-processing, data augmentation, and real-time visualization of training progress.
Author: François Chollet
Citation: Chollet, F. et al. (2015). Keras. URL: https://keras.io.

Why popular: Similar to its R counterpart, the Python implementation of XGBoost is an efficient and powerful gradient boosting algorithm that has demonstrated strong performance on various machine learning tasks.
Important functionalities: Gradient boosting for classification and regression, handling of missing data, parallel and distributed computing support.
Author: Tianqi Chen
Citation: Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794. URL: http://dx.doi.org/10.1145/2939672.2939785.

Why popular: Developed by Facebook, PyTorch is a popular open-source deep learning library known for its flexibility, ease of use, and dynamic computation graph capabilities.
Important functionalities: Support for various neural network architectures, distributed computing, GPU acceleration, and a vast ecosystem of tools and libraries.
Authors: PyTorch developers, Facebook AI Research
Citation: Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., & Chintala, S. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems, 32, 8024-8035. URL: https://papers.nips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html.