Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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Recurrent neural networks without a PhD

Martin Gorner (Google)
9:00am12:30pm Tuesday, March 26, 2019
Average rating: ****.
(4.50, 4 ratings)

Who is this presentation for?

  • Software developers and data scientists



Prerequisite knowledge

  • A basic understanding of computer science, algebra (matrix multiplication), and Python
  • Familiarity with TensorFlow and neural networks (useful but not required)

Materials or downloads needed in advance

  • A laptop with the Jupyter Notebook installed (Detailed setup instructions will be sent before the tutorial.)

What you'll learn

  • Explore RNN basic mechanisms
  • Learn how to do RNN training, use stateless RNNs, and use stateful RNNs for time series extrapolation


The hottest topics in computer science today are machine learning and deep neural networks. Many problems deemed “impossible” only five years ago have now been solved by deep learning, from playing Go to recognizing what’s in an image to translating languages. Software engineers are eager to adopt these new technologies as soon as they come out of research labs. Here’s your chance to learn how.

Martin Gorner leads a hands-on introduction to recurrent neural networks and TensorFlow. You’ll explore the basics of stateless and stateful RNNs and discover how they can be used in time series analysis. Along the way, you’ll learn tips, engineering best practices and pointers to apply in your own projects—no PhD required.

Photo of Martin Gorner

Martin Gorner


Martin Gorner is a developer advocate at Google, where he focuses on parallel processing and machine learning. Martin is passionate about science, technology, coding, algorithms, and everything in between. He spent his first engineering years in the Computer Architecture Group of ST Microelectronics, then spent the next 11 years shaping the nascent ebook market at Mobipocket, which later became the software part of the Amazon Kindle and its mobile variants. He’s the author of the successful TensorFlow Without a PhD series. He graduated from Mines Paris Tech.