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

In-Person Training
Learning TensorFlow

Tom Hope (Independent), Itay Lieder (Independent), Yehezkel Resheff (Independent)
Monday, May 8 & Tuesday, May 9, 9:00am - 5:00pm
Location: Meeting Room 7
Level: Intermediate
Average rating: ****.
(4.00, 1 rating)

Participants should plan to attend both days of this 2-day training course. Platinum and Training passes do not include access to tutorials on Monday and Tuesday.

Tom Hope, Itay Lieder, and Yehezkel Resheff walk you through the fundamental concepts of deep learning and explain how to build production-ready AI systems with TensorFlow, the leading software framework for building machine intelligence systems with deep learning.

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

  • Gain an overview of practical and fundamental deep learning topics
  • Explore TensorFlow—what it is, who is using it, and for what
  • Learn how to build and deploy deep learning solutions with TensorFlow
  • Learn how to visualize your models and better understand training and performance

This training is for you because...

  • You are an engineer in a data-related field (big data, machine learning, data science, etc.) who wants to get a better understanding of TensorFlow.


  • Basic knowledge of Python
  • A general understanding of machine-learning terms and concepts

Hardware and/or installation requirements:

  • A laptop with Python, Git, and the following libraries installed: TensorFlow, Jupyter, Matplotlib, Keras, pandas, and scikit-learn (Installing TensorFlow inside a virtualenv or using Anaconda, which already contains some of the required libraries for this training, is recommended.)
  • The pretrained GloVe word embeddings downloaded prior to the training
  • Participants should download these images and labels (1 & 4)
  • Participants should run this script which will download some datasets needed
  • Code for the training will be available in this Git repo (participants MUST clone it just before the first session)
  • Lastly, please download the representation file for the flower image dataset

Deep learning has emerged as the premier technology in all things AI. With open source frameworks making this technology widely available, deep learning is rapidly becoming a must-know for anybody in big data and machine learning.

Tom Hope, Itay Lieder, and Yehezkel Resheff, the authors of O’Reilly’s upcoming book Learning TensorFlow: A Guide to Building Deep Learning Systems, walk you through the fundamental concepts of deep learning and explain how to build production-ready AI systems with TensorFlow, Google’s open source deep learning framework, which is currently the leading software framework for building machine intelligence systems with deep learning.


Day 1

9:00am – 10:00am: Intro

  • Course logistics and introduction
  • What is machine learning?
  • From ML to DL
  • What is TensorFlow, and what is it good for?

10:00am – 10:30am: Getting started

  • Installing TensorFlow
  • Running the “Hello, World” example and a couple other simple examples

10:30am – 11:00am: Morning break

11:00am – 12:30pm: Internals—Computation graphs

  • What are computation graphs?
  • TensorFlow’s computation graph model
  • Hands-on exercise

12:30pm – 1:30pm: Lunch

1:30pm – 3:00pm: CNNs

  • What are convolutional neural networks?
  • Building your first CNN in TensorFlow
  • Hands-on exercise

3:00pm – 3:30pm: Afternoon break

3:30pm – 5:00pm: Visualization with TensorBoard

  • Lecture and hands-on exercise

Day 2

09:00am – 10:30am: Abstraction layers-slim

  • Lecture and hands-on exercise

10:30am – 11:00am: Morning break

11:00am – 12:30pm: Pretrained vision models

  • Lecture and hands-on exercise

12:30pm – 1:30pm: Lunch

1:30pm – 3:00pm: TensorFlow for text—Part I

  • Intro
  • Basic models (RNN and word embeddings)

3:00pm – 3:30pm: Afternoon break

3:30pm – 4:00pm: TensorFlow for text—Part II

  • RNNs continued
  • Hands-on exercise

4:00pm – 4:40pm: Additional topics

  • Queues, threads, and reading data
  • Distributed TensorFlow
  • Serving models

4:40pm – 5:00pm: Final remarks

  • Recap
  • Where to go from here to further learn the subject?
  • Q&A

About your instructors

Tom Hope is an applied machine-learning researcher and data scientist. Tom has an extensive background as a senior data scientist for large, international corporations, where he has led data science and deep learning R&D across multiple domains, including web mining, text analytics, computer vision, sales and marketing, the IoT, financial forecasting, and large-scale manufacturing. Previously, Tom was at ecommerce startup Tapingo in its early days, where he led data science R&D. He has also served as a data science consultant for major international companies and startups. Tom’s academic research and publications in computer science, data mining, and statistics revolve around machine learning, deep learning, NLP, weak supervision, and time series.

Itay Lieder is a computational neuroscience researcher who uses advanced machine-learning tools to understand and model auditory perception. Itay has industry experience as a data scientist in deep learning R&D, focusing on text mining and NLP.

Yehezkel Resheff is an applied machine-learning researcher with years of experience in both academic and industrial settings. Currently, Yehezkel is working on bringing deep learning into new domains. Previously, Yehezkel was an applied researcher at Intel and Microsoft. His graduate work focused on machine learning for wearable devices.

Conference registration

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

Comments on this page are now closed.


Picture of Audra Carter
04/13/2017 11:19am CDT

Thank you for catching that, Craig. This has been updated.

Craig Nicholson | THINKER
04/11/2017 1:47pm CDT

The Pretrained Vision Model link needs a correction.
current: ..url/vgg_16_2016_08_28.ta.gz
corrected: ..url/vgg_16_2016_08_28.tar.gz

full url :

Sergio Arroyo |
03/05/2017 3:38pm CST

would you have anything like this in an e-learning format? would love to attend cheers