Table 2

Various frameworks for DL implementation

No.Framework namesLink to accessPrimary programming languageOther programming languageMain application field*
1TensorFlowLink to tensorflowLink to the wbsite of tensorflowPythonC++, Java, Go and SwiftCV, NLP, SR, RL
2KerasLink to kerasLink to the website of kerasPythonRCV, NLP, RP
3PyTorchLink to pytorchLink to the website of pytorchPythonC++, Java and JuliaCV, NLP, GM, RL
4CaffeLink to caffe.berkeleyvisionLink to the website of caffe.berkeleyvisionC++Python and MATLABCV, OD, S
5MXNetLink to mxnet.apacheLink to the website of mxnet.apachePython, C++, R, Julia, Scala, Perl and MATLABCV, NLP, RS
6TheanoLink to deeplearningLink to the website of deeplearningPythonC and MATLABCV, NLP, SR
7TorchLink to torchLink to the website of torchLuaPython (PyTorch) and R (Torch7)CV, NLP
8Microsoft Cognitive Toolkit (CNTK)Link to microsoftLink to the website of microsoft.Python, C++ and C#CV, SR, NLP
9ChainerLink to chainerLink to the website of chainerPythonC++CV, NLP, RL
10DeepLearning4jLink to deeplearning4jLink to the website of deeplearning4jJava and ScalaJVM languages like Kotlin and ClojureFD, CBA
11PaddlePaddleLink to paddlepaddleLink to the website of paddlepaddlePython and C++CV, NLP, RS
Note(s):

* CV = Computer vision; NLP = natural language processing; SR = speech recognition; RL = reinforcement learning; RP = rapid prototyping; GM = generative models; OD = object detection; S = segmentation; RS = recommendation systems; FD = fraud detection; CBA = customer behavior analysis

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