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In addition, this release adds new and important deep learning capabilities that simplify the way engineers, researchers, and other domain experts design, train, and deploy models. The MATLAB internal benchmark test shows that the CUDA code generated by GPU coder is 7 times higher than tensorflow and 4.5 times higher than caffe2. The CUDA code converted by GPU coder can directly and efficiently perform inference without matlab environment. In fact, another highlight of matlab2017b this time is the new component “GPU coder”, which can automatically convert the deep learning model code into NVIDIA GPU CUDA code.
New and important deep learning functions can simplify the way engineers, researchers and experts in other fields design, train and deploy models. The updated version fully supports the deep learning development process from data annotation, model building, training and inference, and the final model deployment. MathWorks launched the second half of 2017 version – MATLAB 2017b( matlab r2017b), which includes several new functions of MATLAB and Simulink, six new products and updates and patches for other 86 products.