Deep learning has largely been relegated to powerful desktop computers or servers due to the inherently high memory and processing power requirements of the most successful models. Interest in executing deep learning models on model devices has led to a lot of research on how to either reduce the memory and processing requirements or develop models that implicitly have lower such requirements. MobileNet is one of the models to come from this research. This talk is the first of three parts that we will present on MobileNet. In this part we will discuss the history and significance of MobileNet. Parts two and three will be presented at the Downtown Coding SLC and Salt Lake City PyData meet-ups respectively.
Dustin Webb is an accomplished Deep Learning researcher whose resume includes working alongside Ian Goodfellow and Yoshua Bengio. He began developing intranet applications and worked up to designing, developing, and maintaining large and complex cloud applications. For the last 10 years he has focused on developing his knowledge of artificial intelligence and machine learning. He recently graduated from the University of Utah with a master's in computing with emphasis on deep learning and robotics. More recently he started AI Influx, LLC help clients that need custom software developed, particularly those involving artificial intelligence techniques and technologies with emphasis in deep learning.
7:15pm stick around and mingle