There are several popular deep learning libraries for developing deep learning scripts in Python. Here are some of the most widely used:

TensorFlow: Developed by Google, TensorFlow is one of the most popular and widely used deep learning libraries. It offers a range of tools for building and training deep neural networks and supports both CPU and GPU computing.

PyTorch: Developed by Facebook, PyTorch is another popular deep learning library that provides a dynamic computational graph approach to building neural networks. It is known for its ease of use and flexibility.

Keras: Keras is a high-level deep learning API that is built on top of TensorFlow. It offers a user-friendly interface for building and training neural networks and supports both CPU and GPU computing.

MXNet: Developed by Apache, MXNet is a highly scalable deep learning library that supports multiple programming languages. It is known for its speed and efficiency in training deep neural networks.

Caffe: Caffe is a deep learning library that is primarily designed for computer vision tasks. It is known for its speed and efficiency in processing large amounts of image data.

Ultimately, the choice of which library to use will depend on your specific needs and preferences, as well as the type of deep learning tasks you are working on.
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