Workshop – Deep Learning in Practice: A Hands-On Introduction
Monday, June 4, 2018 in Las Vegas
Full day: 8:30am – 4:30pm
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.
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.
The instructors for this workshop, “Deep Learning in Practice,” lead 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, the instructors 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
Each workshop participant is required to bring their own laptop running Windows 10 – options to rent an on-site Windows laptop for the day will also be provided.
Detailed instructions for 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 provided at 12:30pm – 1:15pm
- Afternoon Coffee Break at 3:00pm – 3:30pm
- End of the Workshop: 4:30pm
Coffee breaks and lunch are included.
James McCaffrey, Microsoft Research and Ricky Loynd, Microsoft Research
Ricky Loynd is a member of the deep reinforcement learning research group in the Microsoft Research AI labs in Redmond, Wash. He has 25 years of experience with neural networks, and 11 years of experience in reinforcement learning. Ricky is currently focused on creating deep RL agents with more general, human-like intelligence. As a lead mentor for the Microsoft AI School Advanced Projects course, Ricky has helped teams throughout the company incorporate deep learning systems into their work.
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.