Fueling innovative software
July 15-18, 2019
Portland, OR

Machine learning with TensorFlow: From linear algebra to neural networks

Rich Ott (The Data Incubator)
Monday, July 15 through Tuesday, July 16,
9:00am - 5:00pm
Location: D137

Participants should plan to attend both days of training course. Note: to attend training courses, you must be registered for a Platinum or Training pass; does not include access to tutorials on Monday or Tuesday.

Incorporating machine learning capabilities into software or apps is quickly becoming a necessity. Rich Ott leads you through two days of intensive learning that include a review of linear algebra essential to machine learning, an introduction to TensorFlow, and a dive into neural networks.

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

  • Understand TensorFlow's capabilities
  • Learn machine learning basic concepts

This training is for you because...

  • You're an engineer or programmer with a background in Python, and you want to develop a basic understanding of machine learning.
  • You have experience modeling or a background in data science, and you want to learn TensorFlow.

Prerequisites:

  • A working knowledge of Python
  • Familiarity with matrices and linear algebra

Hardware and/or installation requirements:

  • A laptop with a web browser

Incorporating machine learning capabilities into software or apps is quickly becoming a necessity rather than a “nice to have” feature, and the open source machine learning framework TensorFlow is becoming an industry standard.

Rich Ott leads you through two days of intensive learning that include a review of linear algebra essential to machine learning, an introduction to TensorFlow, and a dive into neural networks. You’ll master simple machine learning models such as classification and regression models, construct and launch graphs in TensorFlow by using TensorBoard to visualize workflow, and build and test models in TensorFlow using real-world data. You’ll leave with both a theoretical and practical understanding of the algorithms behind machine learning and be ready to incorporate them into your next project.

The class will be taught using TensorFlow’s Python interface.

Outline

Day 1

  • Practical linear algebra
  • Introduction to TensorFlow
  • Iterative algorithms

Exercises

  • Implementing a basic graph
  • Reducing tensors of arbitrary shape
  • Fibonacci numbers
  • Minimizing functions

Day 2

  • Machine learning
  • Basic neural networks
  • Deep neural networks

Exercises

  • Multidimensional linear regression
  • Tuning hyperparameters and visualizing the weight matrix
  • Build an Iris classifier
  • Adding neurons and layers to a neural network
  • Implementing early stopping
  • Exploring activation functions, dropout, and learning rates

About your instructor

Photo of Rich Ott

Richard Ott is a data scientist in residence at the Data Incubator, where he gets to combine his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Conference registration

Get the Platinum pass or the Training pass to add this course to your package. .

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Comments

Picture of Rich Ott
Rich Ott | DATA SCIENTIST IN RESIDENCE
05/13/2019 7:49am PDT

I’m sorry, I missed your second question.

Yes, we let you download the material for the course – including whatever work you’ve done on it.

Abhay Agnihotri | DIRECTOR
04/24/2019 7:43am PDT

Thank you. If nothing is being done locally, will I still be able to take my labs and code with me for reference later on?

Picture of Rich Ott
Rich Ott | DATA SCIENTIST IN RESIDENCE
04/20/2019 7:44am PDT

Nothing needs to be installed on your laptop, we’ll be providing a cloud server setup. You just need a web browser. We’ve found Chrome and Firefox work best

Abhay Agnihotri | DIRECTOR
04/19/2019 3:15pm PDT

What are the prereqs needed to be installed on the laptop?