Machine Learning for Poets: The eMiLy App
Jen Looper explores the possibilities and limitations of machine learning and natural language processing.
It feels like everyone is talking about AI these days – and what’s worse, everyone seems to have a different term for it! “Deep Learning”, “Machine Learning”, “Artificial Intelligence”: what do they all mean, really? Are the concerns about ethical algorithms and automation of jobs grounded in fact, or are they overhyped? And what language will SkyNet be implemented in? (My money’s on JavaScript.)
In this session we’ll answer those questions – and walk through what machine learning really is: how to build a model by ingesting data, training on it, testing, and then deploying to production. We’ll also discuss which tools are primarily used by data scientists; how much math is really required to run predictive models; and some strategies you can use today to incorporate artificial intelligence into your applications.
Paige Bailey is the product manager for Swift for TensorFlow.
Prior to her role as a PM in Google Brain, Paige was developer advocate for TensorFlow core; a senior software engineer and machine learning engineer in the office of the Microsoft Azure CTO; and a data scientist at Chevron. Her academic research was focused on lunar ultraviolet, at the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, CO, as well as Southwest Research Institute (SwRI) in San Antonio, TX.
Jen Looper explores the possibilities and limitations of machine learning and natural language processing.
What is machine learning and artificial intelligence.
Matt Dupree will walk through how gradient descent can be used to form predictions in a machine learning models.
Building serverless and data-driven applications with the Go programming language.
Building serverless and data-driven applications with the Go programming language.