Learn how to build, implement, and deploy a deep learning solution using optimized IA hardware and software
Learn how to build, implement, and deploy a deep learning solution using optimized IA hardware and software. You’ll dive in to dataset wrangling and training before learning how to deploy your model for inference on various architectures.
Day 1: Data and training
Day 2: Inference
Ben Odom leads Intel’s Technical Developer Evangelist team, focused on highlighting, training and showcasing Intel products and tools to developers worldwide. Currently, a portion of Ben’s team have been heavily focused on Artificial Intelligence, developing coursework for Intel’s developer ecosystem and then delivering trainings for both industry and academic developers interested in using Intel’s optimized frameworks and libraries. Ben has been in the tech industry for over 20 years, and has a Master’s Degree in Computer Science and Engineering from Oregon Health Sciences University.
Michael Hernandez is an AI Developer Evangelist within the Software and Services Group at Intel. He works on promoting various Intel technologies that pertain to machine learning and artificial intelligence and regularly speaks at universities to help spread knowledge of AI. Michael holds a bachelor’s degree in computer science from Oregon State University and a master’s degree in computer science from the Georgia Institute of Technology. He can often be found playing board games and lounging with his wife and cat in his free time.
Rudy Cazabon has a Bachelors degree in Space Science (minor in Mechanical Engineering) from the Florida Institute of Technology; with graduate studies in Aerospace and Astronautics from Georgia Tech and Management Science from Stanford. Rudy has served as engineering manager and architect on projects such as Autodesk 3DS Max, Havok StudioTools, and the Project Offset game-engine slated for the then Intel Larrabee graphics architecture.
Meghana Rao is a Developer Evangelist with the Software and Services group at Intel. With her current focus on Artificial Intelligence, she works with universities and developers at large at evangelizing Intel’s AI portfolio and solutions helping them understand Machine Learning and Deep Learning concepts, building models using Intel optimized frameworks and libraries like Caffe*, Tensorflow* and Intel® Distribution of Python*. She has a Bachelor’s degree in Computer Science and Engineering and a Master’s degree in Engineering and Technology Management with past experience in embedded software development, Windows* app development and UX design methodologies.
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