Webinar: Build Your Next Deep Learning Application for NVIDIA Jetson in MATLAB

Learn how you can use MATLAB to build your computer vision and deep learning applications and deploy them on NVIDIA Jetson.

MATLAB auto-generates portable CUDA code that leverages CUDA libraries like cuBLAS and cuDNN from the MATLAB algorithm, which is then cross-compiled and deployed to Jetson.

The generated code is highly optimized and benchmarks will be presented that show that deep learning inference performance of the auto-generated CUDA code is ~2.5x faster for MXNet, ~5x faster for Caffe2 and ~7x faster for TensorFlow.

Date & Time: Wednesday, Oct 4, 2017 from 10:00am – 11:00am PT

By attending this webinar, you’ll learn how to

  1. Access and manage large image sets
  2. Visualize networks and gain insight into the training process
  3. Import reference networks such as AlexNet and GoogLeNet
  4. Automatically generate portable and optimized CUDA code from the MATLAB algorithm

Register now >

About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter