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TensorFlow: Developed by Google Brain, TensorFlow is an open-source machine learning framework widely used for both research and production. PyTorch: Developed by Facebook's AI Research lab (FAIR), PyTorch is known for its dynamic computation graph, making it more intuitive and easier to debug. Keras: Initially a separate high-level API, Keras has been integrated into TensorFlow as its official high-level API. It provides a user-friendly interface for building and training deep learning models. Theano: Although its development has officially ceased, Theano was an influential numerical computation library that played a significant role in the early development of deep learning. Caffe: Developed by the Berkeley Vision and Learning Center (BVLC), Caffe is a deep learning framework that is popular for its expressive architecture and speed. MXNet: Apache MXNet is an open-source deep learning framework that supports both symbolic and imperative programming. It is known for its scalability and efficiency. Chainer: Chainer is a flexible and intuitive deep learning framework that allows dynamic neural network construction in a more imperative style. Deeplearning4j: Deeplearning4j is an open-source deep learning library for Java and Scala, designed for distributed computing and integration with the Java Virtual Machine (JVM). Microsoft Cognitive Toolkit (CNTK): CNTK is a deep learning framework developed by Microsoft that offers efficient training and evaluation of deep learning models. PaddlePaddle: Also known as Paddle, this deep learning framework is developed by Baidu and is popular in China. It supports various deep learning tasks and has a focus on flexibility and ease of use.
There are several deep learning frameworks and tools available that facilitate the development, training, and deployment of deep neural networks. Some popular deep learning frameworks include:
Please note that the field of deep learning is dynamic. It's always a good idea to check the official websites and community forums for the latest information for each of these tools mentioned.
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Sundus F Hantoosh
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"Widely-used DL frameworks, such as PyTorch, TensorFlow, PyTorch Geometric, DGL, and others, rely on GPU-accelerated libraries, such as cuDNN, NCCL, and DALI to deliver high-performance, multi-GPU-accelerated training."
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