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Model scheduling effects and implement pluggable components for software environments Real-Time Software Modeling with Simulink A new Text Analytics Toolbox product, extensible datastore, more big data plots and algorithms for machine learning, and Microsoft Azure blob storage support In addition to deep learning, R2017b also includes a series of updates in other key areas, including: Models can be developed from scratch, including using CNNs for image classification, object detection, regression, and more. Together with capabilities introduced in R2017a, pretrained models can be used for transfer learning, including convolutional neural networks (CNN) models (AlexNet, VGG-16, and VGG-19), as well as models from Caffe (including Caffe Model Zoo).

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Internal benchmarks show the generated code for deep learning inference achieves up to 7x better performance than TensorFlow and 4.5x better performance than Caffe2 for deployed models. A new product, GPU Coder, automatically converts deep learning models to CUDA code for NVIDIA GPUs. In addition to object detection workflows, the toolbox now also supports semantic segmentation using deep learning to classify pixel regions in images and to evaluate and visualize segmentation results. The Image Labeler app in Computer Vision System Toolbox now provides a convenient and interactive way to label ground truth data in a sequence of images. Neural Network Toolbox has added support for complex architectures, including directed acyclic graph (DAG) and long short-term memory (LSTM) networks, and provides access to popular pretrained models such as GoogLeNet.

Specific deep learning features, products, and capabilities in R2017b include: You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications. MATLAB provides a number of features for documenting and sharing your work. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas. You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. MATLABВ® is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation.