scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language.
TensorFlow is an open source library for numerical computation, specializing in machine learning applications. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research.
PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing. It is primarily developed by Facebook's AI Research lab.
Keras is a minimalist, highly modular neural networks library written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
Aesara is a library for defining, optimizing and efficiently evaluating mathematical expressions involving multi-dimensional arrays. Aesara is based on the Theano library.
JAX is a combination Autograd, which lets you take derivatives and compute gradients, in a very fast efficient way and XLA, which lets you compile operations on tensors, using the JIT just-in-time compiler to create really fast code.
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