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This program requires intermediate knowledge of Python, and experience with Deep Learning, Command Line, and OpenCV.See detailed requirements.
Leverage a pre-trained model for computer vision inferencing. You will convert pre-trained models into the framework agnostic intermediate representation with the Model Optimizer, and perform efficient inference on deep learning models through the hardware-agnostic Inference Engine. Finally, you will deploy an app on the edge, including sending information through MQTT, and analyze model performance and use casesDeploy a People Counter at the Edge
Grow your expertise in choosing the right hardware. Identify key hardware specifications of various hardware types (CPU, VPU, FPGA, and Integrated GPU). Utilize the Intel® DevCloud for the Edge to test model performance and deploy power-efficient deep neural network inference on on the various hardware types. Finally, you will distribute workload on available compute devices in order to improve model performance.Design a Smart Queuing System
Learn how to optimize your model and application code to reduce inference time when running your model at the edge. Use different software optimization techniques to improve the inference time of your model. Calculate how computationally expensive your model is. Use the DL Workbench to optimize your model and benchmark the performance of your model. Use a VTune amplifier to find and fix hotspots in your application code. Finally, package your application code and data so that it can be easily deployed to multiple devices.Build a Computer Pointer Controller
Community Manager - IoT Developer Program at Intel®
Stewart is a Technical Evangelist for Intel®, responsible for running workshops, creating content, and supporting the developer community in IoT. He is skilled in developing applications that interface hardware with software for computer vision, robotics, and language processing.
Senior Curriculum Manager at Udacity
After beginning his career in business, Michael utilized Udacity Nanodegree programs to build his technical skills, eventually becoming a Self-Driving Car Engineer at Udacity before switching roles to work on curriculum development for a variety of AI and Autonomous Systems programs.
Graduate Student at the Nanyang Technological University
Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.
Graduate Student at the National University of Singapore
Archana is a graduate student at NUS. She is currently pursuing her research in Deep Learning and Smart Grids, under Professor Dipti Srinivasan. Archana is an Intel® Software Innovator and a former Deep Learning Engineer at Saama Technologies.
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