Presented By O'Reilly and Cloudera
Make Data Work
Dec 4–5, 2017: Training
Dec 5–7, 2017: Tutorials & Conference
Singapore

Getting started with TensorFlow

Yufeng Guo (Google)
9:00am12:30pm Tuesday, December 5, 2017
Data science and advanced analytics, Machine Learning
Location: 328/329 Level: Intermediate

Who is this presentation for?

  • Developers interested in machine learning

Prerequisite knowledge

  • A basic understanding of Python
  • Familiarity with machine learning (useful but not required)

Materials or downloads needed in advance

What you'll learn

  • Learn how to build and deploy simple and complex models with TensorFlow

Description

Yufeng Guo walks you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng takes you from a conceptual overview all the way to building complex classifiers and explains how you can apply deep learning to complex problems in science and industry.

Third-party libraries used:

  • TensorFlow—A machine learning library written in C++ with a robust Python API
  • pandas—An open source library providing high-performance, easy-to-use data structures and data analysis tools for Python
  • The Jupyter Notebook—An open source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text

Outline

Machine learning and TensorFlow

  • What is ML, and why do we care?
  • Why is TensorFlow uniquely good or useful for ML?

A wide and deep thought experiment

Wide and deep code model

  • Input functions
  • Create, train, eval, predict loop
  • Run the code in Jupyter

Additional info

  • TensorBoard visualizations of the training and model graph
  • Limitations of this model

Diving into a lower level of TensorFlow

  • Using MNIST as a toy dataset to play with model structure
  • TensorFlow primitives

Creating a simple network by hand

  • Using the core TF libraries to create a model for solving MNIST
  • Tips and tricks for improving your model

Upgrading the model to a CNN (time permitting)

  • Creating CNN layers by hand
  • Available hyperparameters

Wrap-up and Q&A

  • Other models for other problem domains
  • Production environment considerations
  • Resources
Photo of Yufeng Guo

Yufeng Guo

Google

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.

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