Making Open Work
May 8–9, 2017: Training & Tutorials
May 10–11, 2017: Conference
Austin, TX

Go deep, go wide, go everywhere: Hands-on machine learning with TensorFlow

Yufeng Guo (Google), Amy Unruh (Google)
1:45pm3:15pm Wednesday, May 10, 2017
Data, Big and Small, TensorFlow
Location: Meeting Room 16
Level: Intermediate
Average rating: ****.
(4.67, 6 ratings)

Who is this presentation for?

  • Software developers, software engineers, and data scientists

Prerequisite knowledge

  • Familiarity with machine-learning basics

What you'll learn

  • Understand how to use deep learning to train a model, how to select features for your model, and how to design new, useful features


Deep learning has already revolutionized machine-learning research, but it remains opaque to many developers. Yufeng Guo and Amy Unruh explain just how easy it is to get started with advanced machine learning by live-coding a wide and deep learning model—a deep neural network classifier—training it using TensorFlow’s tf.learn library, and evaluating it. Along the way, Yufeng and Amy cover feature selection, feature crosses, bucketing and the hashing trick, categorical versus continuous features and how to change between them, hidden units, and hyperparameter tuning. You’ll leave ready to use deep learning on your own data.

Photo of Yufeng Guo

Yufeng Guo


Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him on Twitter.

Photo of Amy Unruh

Amy Unruh


Amy Unruh is a developer programs engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics, as well as other Cloud Platform technologies. Amy has an academic background in CS/AI, and she’s worked at several startups as well as industrial R&D and published a book on App Engine.