Deep learning frameworks are essential tools for building and deploying deep learning models. They provide a comprehensive set of functionalities to simplify the process of training and deploying deep learning models. Below are some popular deep learning frameworks:
TensorFlow: TensorFlow is an open-source machine learning framework developed by Google Brain. It is widely used for various applications, including image recognition, natural language processing, and reinforcement learning. Learn more about TensorFlow.
PyTorch: PyTorch is another popular deep learning framework developed by Facebook AI Research. It is known for its ease of use and dynamic computational graph, making it a preferred choice for research and development.
Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It is designed to enable fast experimentation with deep neural networks.
Caffe: Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
MXNet: MXNet is a flexible and efficient library for deep learning that allows you to define, train, and deploy deep neural networks on a wide variety of devices, from mobile devices to servers with hundreds of GPUs.
Microsoft Cognitive Toolkit (CNTK): CNTK is Microsoft's open-source deep learning toolkit that enables developers and researchers to train, optimize, and deploy deep learning models.
For further reading, you can explore the documentation of these frameworks or dive into practical tutorials. Happy learning!