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.
Potential topics within this talk include the following:
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
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
Principal Application Engineer, TechSource Systems
Ian M. Alferez is the Principal Application Engineer at TechSource Systems. He specializes in in the field of embedded system (embedded coder configuration), data analytics (Machine Learning) and technical computing with Matlab/Simulink. He holds a Bachelor of Science in Electronics and Communication Engineering from the University of San Carlos in Cebu, Philippines. Before joining Techsource Asia, he worked as a Software Development Engineer at Lear Corporation where he refined his skills in Model Based Design with regards to the Verification and Validation Workflow and Embedded Software / Hardware.
He has built his forte in Process Automation with Matlab, Production Code Customization, Optimization and Generation with Embedded Coder, Matlab/Simulink Algorithm for Auto Code Generation and Hardware Target Deployment, Customizing the Auto Test Generation / Property Proving with Simulink Design Verifier.
Topic 2: Image Processing with MATLAB
10.00 - 10.55
Topic 1: MATLAB & Simulink Fundamentals
9.30 - 10.00
Topic 3: Machine Learning with MATLAB
Topic 4: MATLAB support package for Raspberry Pi or Arduino
12.30 - 13.00
Topic 5: Systems Identification App for Mathematical Models of Dynamic Systems Construction.