Brian Sletten
From Bosatsu Consulting, Inc.
Brian Sletten is a liberal arts-educated software engineer with a focus on forward-leaning technologies. His experience has spanned many industries including retail, banking, online games, defense, finance, hospitality and health care. He has a B.S. in Computer Science from the College of William and Mary and lives in Auburn, CA. He focuses on web architecture, resource-oriented computing, social networking, the Semantic Web, data science, 3D graphics, visualization, scalable systems, security consulting and other technologies of the late 20th and early 21st Centuries. He is also a rabid reader, devoted foodie and has excellent taste in music. If pressed, he might tell you about his International Pop Recording career.
TensorFlow
This open source machine learning framework from Google has taken off. Come learn what you can do with it in your own organization.
TensorFlow is a powerful data flow-oriented machine learning framework developed by Google's Brain Team. It was designed to be easy to use and widely applicable on both numeric, neural network-oriented problems as well as other domains. We'll cover the over view as well as apply it to several fun, realistic problems.
WebAssembly
What happens if web applications got really fast?
We are increasingly able to do more in the browser because of faster networks, optimized JavaScript engines, new standard APIs and more. There is a new initiative to allow a binary format called WebAssembly that will provide a compiled, cross-platform representation that will take us to the next level. Complex business applications and 3D video games will alike will benefit from this new standard. The implications for the Web are probably greater than you can imagine.
TensorFlow
This open source machine learning framework from Google has taken off. Come learn what you can do with it in your own organization.
TensorFlow is a powerful data flow-oriented machine learning framework developed by Google's Brain Team. It was designed to be easy to use and widely applicable on both numeric, neural network-oriented problems as well as other domains. We'll cover the over view as well as apply it to several fun, realistic problems.