Workshop – Deep Learning in Practice: A Hands-On Introduction

Monday, June 20, 2022 – Caesars Palace, Las Vegas

Full day: 8:30am – 4:30pm

Important note: Each workshop participant is required to bring or rent their own laptop running Windows 10. Although this workshop requires Windows 10, the skills learned and code used will be transferrable to Mac OS and other platforms. See below for more information.

Intended Audience: Anyone who wishes to learn how to create deep learning systems using TensorFlow, Keras, and other popular software libraries.

Knowledge Level: Basic knowledge of machine learning terminology. Minimal programming experience with a C-family language such as Python, C/C++, C# or Java is recommended but not required.

Other deep learning workshop: This is one of two full-day workshops on deep learning held at Mega-PAW. These two workshops are complementary – you can opt to take either one alone, or both. Neither is a prerequisite for the other. The other workshop is Hands-On Deep Learning in the Cloud: Fast and Lean Data Science with Tensorflow, Keras, and TPUs.

James McCaffrey
James McCaffrey

Senior Scientist Engineer

“The best workshop I’ve ever attended. Lots of new very useful knowledge about deep neural networks and reinforcement learning!” Jan Bohinec

Senior Market Analyst, GEN-I

“I was able to come away with a lot of new knowledge on ML; walking through the code templates made me confident that I could apply these techniques to my own work.” B.S.

The University of Wisconsin Foundation

“An awesome deep learning workshop – at the best deep learning event in the world.” Syed Ali

Data Scientist, Analytics Led Intelligence

“The workshop was 10 stars out of 10 stars.” A. T.

National Institute of Standards and Technology

More Testimonials
“One of the best workshops I’ve been to.”
S. M.
W. W. Grainger

“I really enjoyed the workshop, and I’m looking forward to applying these techniques in my work.”
J. H.
Blue Cross Blue Shield of North Carolina

“I really enjoyed your topics and breakdowns.”
T. P.
Quicken Loans

“The session was very informative and I was most mind-blown by the LSTM model and how well it worked.”
A. G.

“It was an incredible content and hands on experience, beside all of the best practices and advices you shared with us.”
M. A.
Sodimac Homecenter (Chile)

Workshop Description

This one-day introductory workshop dives deep. You will explore deep neural classification, LSTM time series analysis, convolutional image classification, advanced data clustering, bandit algorithms, and reinforcement learning. It’s a hands-on class; you’ll learn to implement and understand both deep neural networks as well as unsupervised techniques using TensorFlow, Keras, and Python. Just as importantly, you’ll learn exactly what types of problems are appropriate for deep learning techniques, and what types of problems are not well suited to deep learning.

Instructor James McCaffrey leads Microsoft Research’s CEO-mandated initiative to transfer deep learning intelligence into all products, services, and supporting systems across the enterprise. Workshop participants will access much of the same state-of-the-art training material used for this work at Microsoft. Along the way, James will cover case studies detailing large-scale deployments for their internal clients that have generated astounding ROIs.

During the day, workshop attendees will gain the following practical hands-on experience:

  • How to prepare, normalize, and encode data for deep learning systems.
  • How to install deep learning libraries including TensorFlow, Keras, and CNTK, and the pros and cons of each library.
  • How to create deep learning predictive systems for various kinds of data: classical business data, time series data (such as sales data), image data (such as the famous MNIST dataset for handwriting recognition), and text/document data (such as legal contracts). These datasets are a great place to start – however, for the more experienced attendee, even more challenging, “next level” datasets, such as for object recognition, will be optionally available.

This workshop assumes you have a basic knowledge of machine learning terminology but does not assume you are a machine learning expert. Some theory will be presented but only enough to help you understand how to make a practical, working deep learning system. This is a code-based workshop, so some programming experience will be helpful. However, beginners will be able to follow along but may have to work a bit harder to keep up.

Hardware: Bring Your Own Laptop

Important note: Each workshop participant is required to bring or rent their own laptop running Windows 10. Although this workshop requires Windows 10, the skills learned and code used will be transferrable to Mac OS and other platforms because all software used will be open source.

An option to rent an on-site Windows laptop with the required software and datasets pre-installed for the workshop day ($220) is provided during your registration process.

For those who do not rent a laptop, detailed instructions for required software installation will be provided to registered participants a few days before the workshop. Assistants will also be on hand to help attendees with hardware/software issues.

Attendees receive an electronic copy of the course materials and related code at the conclusion of the workshop.


  • Workshop starts at 8:30am
  • Morning Coffee Break at 10:30am – 11:00am
  • Lunch at 12:30pm – 1:15pm
  • Afternoon Coffee Break at 3:00pm – 3:30pm
  • End of the Workshop: 4:30pm


James McCaffrey, Senior Scientist Engineer, Microsoft Research

James McCaffrey works for Microsoft Research in Redmond, Wash. James explores applied deep machine learning and AI. James has a PhD in cognitive psychology and computational statistics from the University of Southern California, a BA in psychology, a BA in applied mathematics, and an MS in computer science. James learned to speak to the public while working at Disneyland as a college student, and he can still recite the entire Jungle Cruise ride narration from memory.

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