Deep Learning with Structured Data with Mark Ryan

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About this Episode

Today we are joined by Mark Ryan, author of Deep Learning with Structured Data. Mark started his journey as many thoughtful people do: trying to solve a problem. While working on the Support team at IBM Data and AI, he created a prototype deep learning model to predict the time of support ticket completion and which tickets would escalate. During this process, he saw that there was a lack of general structured data sets that people could apply their models to. As he was contemplating this problem, Mark noticed that in his hometown of Toronto, the streetcar network was causing problems through gridlock and delays. He created a deep learning model to predict these delays, but more importantly, gathered an open data set that was the perfect size and variety, and catapulted his contemplation into the book it is today. In this episode, Mark shares the benefits of applying deep learning to structured data (and recently reduced barriers to entry), details of his experience with a range of data sets, the everlasting appreciation he and Sam shares for the course by Jeremy Howard, and the contents of his new book, aimed to help set up and maintain deep learning models with structured data. If you would like early access to the book, please use the link below. You will have the opportunity to make comments, provide feedback and give recommendations that will be incorporated into the final published product. Check it out!
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