Comparison of Deep Learning Software Frameworks
This article provides an overview of deep learning as a branch of artificial intelligence and presents detailed tables comparing numerous deep‑learning software frameworks and libraries, covering their creators, release dates, licenses, platforms, languages, APIs, and support for parallelism and hardware acceleration.
Deep Learning (DL) is a subfield of Machine Learning (ML) that aims to enable machines to learn hierarchical representations of data, achieving superior performance in tasks such as image, speech, and text recognition.
The article presents a comprehensive comparison of popular deep‑learning software frameworks and libraries, including Apache MXNet, Apache SINGA, BigDL, Caffe, Chainer, Deeplearning4j, Dlib, Flux, Intel Math Kernel Library, Keras, MATLAB Deep Learning Toolbox, Microsoft Cognitive Toolkit, Neural Designer, OpenNN, PlaidML, PyTorch, TensorFlow, Theano, Torch, and Wolfram Mathematica.
For each tool, the comparison lists creator, initial release year, license type, open‑source status, supported platforms, programming languages, available APIs, and support for OpenMP, OpenCL, CUDA, automatic differentiation, pretrained models, and multi‑node parallel execution.
Additional tables compare model format compatibility (e.g., TensorFlow, ONNX) and summarize licensing notes.
The article concludes with references to community resources and promotional links.
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