1. TensorFlow: TensorFlow is an open-source library developed by Google. It provides a comprehensive ecosystem of tools, libraries, and community resources for building and deploying machine learning models. It supports various programming languages, including Python, C++, and JavaScript.
2. PyTorch: PyTorch is an open-source deep learning framework developed by Facebook's AI Research Lab. It is known for its dynamic computational graph, which allows for flexible and intuitive model building. PyTorch is widely used in both research and production environments.
3. Keras: Keras is a high-level neural networks API written in Python. Initially developed as a user-friendly interface for TensorFlow, Keras gained popularity due to its simplicity and ease of use. It provides a higher-level abstraction for building neural networks, making it suitable for beginners and rapid prototyping.
4. Caffe: Caffe is a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC). It is designed for efficiency and speed, especially in computer vision tasks. Caffe's strengths lie in its pre-trained models and ability to process large datasets efficiently.
5. MXNet: MXNet is an open-source deep learning framework supported by Apache. It offers a flexible programming interface and supports multiple programming languages, including Python, R, and Scala. MXNet is known for its scalability, allowing the distribution of computations across multiple devices.
6. Theano: Theano is a Python library that allows for efficient mathematical computation, especially for deep learning models. It provides a low-level interface for optimizing mathematical expressions and can run computations on both CPUs and GPUs.

Please note that the deep learning landscape is constantly evolving, and new frameworks may have emerged since my last knowledge update. It's always a good idea to explore the latest developments and community preferences in deep learning frameworks.