Demystifying deep learning: A practical approach in MATLAB

Overview

Deep learning, a chief driver of the AI revolution, can achieve state-of-the-art accuracy in many cognitive or perceptual tasks such as naming objects in a scene or recognizing optimal paths in an environment.

It involves assembling large data sets, creating a neural network, and training, visualizing, and evaluating different models, using specialized hardware - often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.

In this seminar, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. We’ll build and train neural networks that recognize handwriting, categorize foods, classify signals, and control machines. 

Highlights

  • Manage large data sets (images, signals, text, etc.)
  • Create, analyze, and visualize networks, and gain insight into the black box nature of deep learning models
  • Automatically label ground truth or generate synthetic data
  • Build or edit deep learning models with a drag-and-drop interface
  • Perform classification, regression, and semantic segmentation with images or signals
  • Apply reinforcement learning with deep Q networks (DQN)
  • Leverage pre-trained models (e.g. GoogLeNet and ResNet) for transfer learning
  • Import models from Keras-TensorFlow, Caffe, and the ONNX Model format
  • Speed up network training with parallel computing on a cluster

About the Presenter

Loren Shure has worked at MathWorks for over 30 years. For the first 27 of these years, Loren co-authored several MathWorks products in addition to adding core functionality to MATLAB, including major contributions to the design of the MATLAB language. She is currently part of the Application Engineering team, enabling Loren to spend more time and energy working with customers.

For more than 10 years, traveling worldwide over half of each year, Loren delivers more than 150 technical, strategic, and vision-setting presentations yearly to audiences ranging from hands-on problem solvers through high-level executives.

Loren graduated from MIT with a B.Sc. in physics and has a Ph.D. in marine geophysics from the University of California, San Diego, Scripps Institution of Oceanography. She is a Senior Member of IEEE; and she is co-author on several patent inventions. Loren writes about MATLAB on her blog, The Art of MATLAB.

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