Matlab and Simulink offer enhanced AI functions in latest release

September 24, 2018 // By Christoph Hammerschmidt
The latest release of the popular Matlab and Simulink math software tools from Mathworks, designated 2018b, contains significant enhancements for deep learning as well as new features and improvements in all product families. With its new Deep Learning Toolbox, the software manufacturer also provides a framework for the design and implementation of neural networks. Developers in the fields of image processing, computer vision and signal processing can use the new features to design complex neural network architectures and improve their deep learning models.

MathWorks recently joined the ONNX community to promote interoperability and enable collaboration between Matlab users and other deep learning frameworks. With the new ONNX conversion feature in R2018b, developers can import and export models from supported frameworks such as PyTorch, MxNet, and TensorFlow. Thanks to this interoperability, models trained in Matlab can also be used in other frameworks.

Likewise, models trained in other frameworks can be integrated into Matlab, where tasks such as debugging, validation, and deployment on embedded platforms can be performed. In addition, R2018b provides carefully selected reference models that can be accessed using a single line of code. Additional import functions allow the use of models from Caffe and Keras-TensorFlow.


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