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Spotlight on Data: The Power of Deep Learning in the Hands of Domain Experts with Jeremy Howard and Hirokazu Narui

An interactive case study from platform.ai and Silicon Valley Innovation Laboratories

Jeremy Howard
Hirokazu Narui

How do you put the power of deep learning into the hands of product managers, business analysts, innovators, educators, and researchers?

Join us for this edition of Spotlight on Data to find out how Silicon Valley Innovation Laboratories—a research center established by Japanese electric and electronics equipment company Furukawa Electric—trained a deep learning model to recognize manufacturing defects in fiber optic cables using platform.ai.

In this case study, Hirokazu Narui, the business development manager at Silicon Valley Innovation Laboratories, will walk you through how he accomplished this without writing any code or needing the help of a data scientist. In just three hours, he learned how platform.ai works and labeled enough images (in this case, 500 labeled from scratch and 500 prelabeled) to get 100% accuracy on a hold-out set. Jeremy Howard, chief scientist at platform.ai, will also be on hand to give a brief overview of the platform.

O’Reilly Spotlight explores emerging business and technology topics and ideas through a series of one-hour interactive events. You’ll engage in a live conversation with experts, sharing your questions and ideas while hearing their unique perspectives, insights, fears, and predictions for the future.

In every edition of Spotlight on Data, you’ll learn about, discuss, and debate the tools, techniques, questions, and quandaries in the world of data. You’ll discover how successful companies leverage data effectively and how you can follow their lead to transform your organization and prepare for the Next Economy.

What you'll learn-and how you can apply it

By the end of this live show, you’ll better understand:

  • How platform.ai can be used to train deep learning models without writing any code
  • Examples of practical applications of deep learning across different domains

This training course is for you because...

  • You're a domain expert facing a problem that would benefit from deep learning and need to know a fast way to get started.


  • Come with your questions for Jeremy Howard and Hirokazu Narui
  • Have a pen and paper handy to capture notes, insights, and inspiration

Recommended follow-up:

About your instructor

  • Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He’s also a faculty member at the University of San Francisco and is chief scientist at doc.ai and platform.ai. Previously, Jeremy was the founding CEO at Enlitic, the first company to apply deep learning to medicine (selected as one of the world’s top 50 smartest companies by MIT Tech Review two years running); president and chief scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions two years running; and the founding CEO of two successful Australian startups—FastMail and Optimal Decisions Group (acquired by Lexis-Nexis). Before that, he spent eight years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups and contributed to many open source projects. He has made many appearances on television and other videos, including as a regular guest on Australia’s highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.

  • Hirokazu Narui is a business development manager at American Furukawa. His main interests are deep learning, machine learning, and signal processing. Previously, he was a visiting researcher in the Department of Computer Science at Stanford University while working at Furukawa Electric. He holds an MS in physics and electronics from Osaka Prefecture University, Japan.


The timeframes are only estimates and may vary according to how the class is progressing

Thursday, August 8, 2019, at 9:00am PT / 12:00pm ET

  • Introduction and presentation (15 minutes)
  • Interactive discussion and Q&A (45 minutes)