Speaker: Mike Houston, Distinguished Engineer, nVidia
Wednesday, June 3, 2015, 2013, 6:30PM ,
at nVidia, 2800 Scott Blvd., Building E, Santa Clara, CA
Refreshments will be provided.
This meeting is Co-Sponsored with Silicon Valley Automotive Open Source, IEEE-VTS, and nVidia
Deep learning refers to algorithms -step-by-step data-crunching recipes- for teaching machines to understand unstructured data, such as images, speech and video. With deep learning, a neural network learns many levels of abstraction. This lets a machine learn what computer scientists call a “hierarchical representation,” and gets smarter the more information is fed into the system. The result is that a deep neural network for driving applications can understand the subtle nuances of what is happening around the vehicle. It can discern an ambulance from a delivery truck, determine whether a parked car is vacant or the door is opening as a passenger emerges, and also detect occluded objects, such as a pedestrian partially blocked by a parked car. GPUs are ideal for training the neural net, and for running the model inside the vehicle in real time. They can cut the time that it takes to train these neural networks to just days from a year or more, as the GPU is a massively parallel processor. Once a system is “trained,” that learning can be used in applications for self-driving cars.
6:30-7:00 PM- Networking and refreshments
7:00- 7:30 PM- Deep Learning (Presented by Michael Houston)
7:30- 8:00 PM- Self-Driving Cars (Presented by TBD)
8:00-8:15 PM- Q&A
8:15-8:30 PM- Networking
100% of proceeds go to benefit San Jose State University's Formula SAE racing team.