Python has several libraries that are commonly used for developing deep learning scripts. Some of the most popular libraries include:

1. TensorFlow: TensorFlow is an open-source machine learning library that was developed by Google. It provides a wide range of tools and resources for building and training deep neural networks.
2. PyTorch: PyTorch is an open-source machine learning library that is maintained by Facebook. It provides dynamic computational graphs and an easy-to-use interface for building and training deep neural networks.
3. Keras: Keras is a high-level neural networks API that is written in Python. It runs on top of TensorFlow, Theano, and Microsoft Cognitive Toolkit, providing a simple interface for building and training deep learning models.
4. MXNet: MXNet is a flexible and efficient deep learning library that is supported by Amazon Web Services (AWS). It is designed to work well with large datasets and can be used for both research and production.
5. Caffe: Caffe is a deep learning framework that was developed by the Berkeley Vision and Learning Center (BVLC). It is optimized for speed and can be used for both research and production.

Each library has its own unique strengths and weaknesses, and the choice of library will depend on the specific needs of your project.
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