Demystifying Deep Learning: A Practical Approach in MATLAB

Demystifying Deep Learning: A Practical Approach in MATLAB

March 15th, 9:30AM to 11:30AM

Instructor: Jianghao Wang

Are you new to deep learning and want to learn how to use it in your work? This seminar demonstrates new MATLAB features that support deep learning tasks. In doing so, we’ll decipher practical knowledge of the domain of deep learning. We’ll build and train neural networks that recognize handwriting, classify food in a scene, and figure out the drivable area in a city environment.

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Event Categories:  Workshops

Deep learning can achieve state-of-the-art accuracy in many humanlike tasks such as naming objects in a scene or recognizing optimal paths in an environment. The main tasks are to assemble large data sets, create a neural network, to train, visualize, and evaluate different models, using specialized hardware - often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.

This seminar demonstrates MATLAB features that make it easy to:

  • Manage extremely large sets of images
  • Visualize networks and gain insight into the black box nature of deep networks
  • Perform classification and pixel-level semantic segmentation on images
  • Import training data sets from networks such as GoogLeNet and ResNet
  • Import and use pre-trained models from TensorFlow and Caffe
  • Speed up network training with parallel computing on a cluster
  • Automate manual effort required to label ground truth
  • Automatically convert a model to CUDA to run on GPUs

Biomdical & Physical Sciences Building, Room 1455-A
567 Wilson Road
East Lansing, MI 48824
United States
Michigan US