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
TensorFlow
Location: Meeting Room 7
Level: Intermediate
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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 introduce TensorFlow, Google’s open source deep learning framework, which is currently 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...

Engineers in data-related fields (big data, machine learning, data science, etc.)

Prerequisites:

  • 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: TensorFlow, jupyter, matplotlib, keras, pandas, sklearn. We recommend installing TensorFlow inside a virtualenv or using Anaconda, which already contains some of the required libraries for this training.
  • Pretrained models (we recommend downloading in advance before the training begins):

Deep learning has emerged in the last few years 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 introduce TensorFlow, Google’s open source deep learning framework, which is currently the leading software framework for building machine intelligence systems with deep learning. The instructors are authors of O’Reilly’s upcoming book, “Learning TensorFlow : A guide to building deep learning systems”.

You will learn the fundamental concepts of deep learning and explore everything from basic examples to advanced topics aimed at building production-ready AI systems, all explained and made practical with TensorFlow.

Schedule

Day 1

09:00am – 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—Get 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

12:30pm – 1:30pm—Lunch

1:30pm – 3:00pm—CNNs

  • What are convolutional neural networks? (Intro and common architectures
  • Building your first CNN in TensorFlow
  • Hands-on

3:00pm – 3:30pm—Afternoon break

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

  • Session + hands-on

Day 2

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

  • Session and hands-on

10:30am – 11:00am—Morning break

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

  • Hands-on

12:30pm – 1:30pm—Lunch

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

  • Intro
  • Basic models (RNN + word embeddings)

3:00pm – 3:30pm—Afternoon break

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

  • RNN continued
  • Hands-on

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, currently using 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

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Comments

Picture of Audra Montenegro
Audra Montenegro | SENIOR SPEAKER MANAGER
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 : http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz

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

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