Besides scikit-learn mentioned earlier, several popular machine learning frameworks and libraries include:
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TensorFlow: An open-source library developed by Google Brain for building and training deep learning models. It provides high-level APIs and supports distributed computing.
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PyTorch: Anotherpopular open-source deep learning library that offers dynamic computation graphs and a Pythonic programming interface. It is widely used in research and industry for building neural networks.
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Keras: A high-level neural networks API written in Python, which can run on top of TensorFlow, Theano, or CNTK. Keras simplifies the process of building and training deep learning models.
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MXNet: A flexible and efficient deep learning framework that supports both imperative and symbolic programming. It is known for its scalability and can run on a variety of devices, including GPUs and distributed systems.
These frameworks provide extensive functionalities for implementing and training complex machine learning models, including deep neural networks.
