MathWorks Partners with NVIDIA’s Deep Learning Institute to offer new Deep Learning with MATLAB course

MathWorks has announced that a comprehensive “Deep Learning with MATLAB” course is now available, developed in collaboration with NVIDIA’s Deep Learning Institute. The two-day course is being offered in instructor-led online and self-paced on-demand formats throughout the rest of 2020. On completion, engineers, scientists and researchers will be ready to apply GPU-accelerated deep learning techniques in MATLAB to common applications such as image classification, autonomous systems, voice recognition, and object detection. For dates and locations, visit the Deep Learning with MATLAB course schedule. 

MathWorks provides a comprehensive platform for building AI-driven systems that is based on decades of supporting complex engineering projects. GPU Coder generates optimized CUDA code from MATLAB code for deep learning, embedded vision, and autonomous systems, which allows developers to build solutions that run efficiently on NVIDIA GPUs. In addition, a MATLAB container from NVIDIA GPU Cloud (NGC), a hub for GPU-optimized AI and HPC software, provides a complete deep learning workflow that uses NVIDIA GPUs to accelerate neural network training to scale up performance across nodes. 

“The NVIDIA Deep Learning Institute plays a crucial role in developing hands-on training and showcasing how to use new techniques like deep learning to solve complex problems,” says David Rich, director, MATLAB marketing, MathWorks. “This course offers a practical approach to deep learning that will help NVIDIA users to iterate quickly and converge on a solution that meets  product and time-to-market requirements.” 

“There’s been a surge of interest in the Deep Learning with MATLAB course using NVIDIA GPUs,” said Will Ramey, senior director and global head of developer programs at NVIDIA. “Learning how to quickly and easily apply the power of NVIDIA GPUs to accelerate neural network training streamlines the process of application development and allows for more rapid deployment and faster time to market.” 

Sources: Press materials received from the company and additional information gleaned from the company’s website.

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