Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Tutorials

On Tuesday, June 27, choose from half-day tutorials. These expert-led presentations give you a chance to dive deep into the subject matter. Please note: to attend, your registration package must include tutorials on Tuesday; does not include access to training courses.

Tuesday, June 27

9:00am12:30pm Tuesday, June 27, 2017
Location: Sutton Center Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Joseph Spisak (Amazon), Sunil Mallya (Amazon Web Services)
Joseph Spisak and Sunil Mallya offer an introduction to the powerful and scalable deep learning framework Apache MXNet. You'll gain hands-on experience using Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development and leave able to quickly spin up AWS GPU clusters to train at record speeds. Read more.
9:00am12:30pm Tuesday, June 27, 2017
Location: Beekman Level: Intermediate
Secondary topics:  Natural Language, User interface and experience
Galiya Warrier (Microsoft), Gary Short (Microsoft)
Galiya Warrier and Gary Short walk you through creating a quantitative graph model from qualitative written information and demonstrate how to add a conversational frontend using the Microsoft Bot Framework. Read more.
9:00am12:30pm Tuesday, June 27, 2017
Location: Murray Hill E/W Level: Advanced
Secondary topics:  Cloud, Deep Learning
Yufeng Guo (Google), Amy Unruh (Google)
Average rating: **...
(2.00, 2 ratings)
Amy Unruh and Yufeng Guo walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Amy and Yufeng begin by giving an overview of TensorFlow and demonstrating some fun, already-trained TensorFlow models. Then, they show how to build a simple classifier in TensorFlow, before introducing some more complex classifier models. Read more.
9:00am12:30pm Tuesday, June 27, 2017
Location: Sutton North Level: Advanced
Secondary topics:  Machine Learning
Average rating: *****
(5.00, 4 ratings)
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka surveys the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts. Read more.
9:00am12:30pm Tuesday, June 27, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Kristian Hammond (Northwestern Computer Science)
Average rating: ****.
(4.00, 12 ratings)
Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making, focusing on how they can be used, the requirements for doing so, and the expectations for their effectiveness. Read more.
1:30pm5:00pm Tuesday, June 27, 2017
Location: Sutton Center Level: Beginner
Secondary topics:  Deep Learning
Yiheng Wang (Intel), Jiao(Jennie) Wang (Intel)
Average rating: **...
(2.50, 2 ratings)
Yiheng Wang and Jennie Wang offer an overview of BigDL, a distributed deep learning library on Apache Spark that helps users easily integrate most advanced deep learning algorithms (CNN, RNN, etc.) into popular big data platforms. Yiheng and Jennie demonstrate how to develop with BigDL and share some practical use cases. Read more.
1:30pm5:00pm Tuesday, June 27, 2017
Location: Beekman Level: Intermediate
Secondary topics:  Deep Learning, Machine Learning
Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft), Patrick Buehler (Microsoft)
Average rating: **...
(2.67, 3 ratings)
Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines. Read more.
1:30pm5:00pm Tuesday, June 27, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Secondary topics:  Machine Learning
Vikash Mansinghka (MIT), Richard Tibbetts (Empirical Systems)
Average rating: ****.
(4.50, 2 ratings)
Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions that previously took hours for trained statisticians. Read more.
1:30pm5:00pm Tuesday, June 27, 2017
Location: Sutton North Level: Beginner
Secondary topics:  Deep Learning
Laura Harding Graesser (New York University)
Average rating: *****
(5.00, 1 rating)
Laura Graesser offers a hands-on introduction to neural networks using the popular Python library Keras, focusing on building intuition for the core components of a neural network and what it means for a network to “learn.” You'll also get the opportunity to build and train your own network. Read more.
1:30pm5:00pm Tuesday, June 27, 2017
Location: Murray Hill E/W Level: Intermediate
Secondary topics:  Deep Learning, Machine Learning
Arthur Juliani (Unity Technologies)
Average rating: *****
(5.00, 2 ratings)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani offers a deep dive into reinforcement learning, from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks. Read more.