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

Schedule: Deep Learning sessions

Artificial intelligence comprises more than deep learning, but this field has become one of the most important areas, with how quickly it has empowered machines to comprehend and generate content—from visual and textual to scientific and artistic.

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9:00am - 5:00pm Monday, June 26 & Tuesday, June 27
Location: Bryant
Charles Killam (NVIDIA)
NVIDIA Deep Learning Institute-certified instructor Charlie Killam walks you through solving the most challenging problems with deep learning. You'll start with deep learning basic concepts and quickly move to talking on real-word problems using deep learning. Read more.
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9:00am - 5:00pm Monday, June 26 & Tuesday, June 27
Location: Morgan
Robert Schroll (The Data Incubator), Michael Li (The Data Incubator), Dana Mastropole (The Data Incubator)
Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow's deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore several real-world deep learning applications, including machine vision, text processing, and generative networks. Read more.
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9:00am - 5:00pm Monday, June 26 & Tuesday, June 27
Location: Clinton
Delip Rao (Joostware)
Delip Rao explores natural language processing using a set of machine-learning techniques known as deep learning. Delip walks you through neural network architectures and NLP tasks and teaches you how to apply these architectures for those tasks. Read more.
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9:00am - 5:00pm Monday, June 26 & Tuesday, June 27
Location: Gibson
Josh Patterson (Skymind), Susan Eraly (Skymind), Tom Hanlon (Skymind)
Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson and Susan Eraly demonstrate how to use Deeplearning4J to build recurrent neural networks for time series data. Read more.
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9:00am12:30pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton Center (Sponsored) Level: Intermediate
Joseph Spisak (Amazon)
Joseph Spisak and Anima Anandkumar 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.
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9:00am12:30pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Advanced
Yufeng Guo (Google), Amy Unruh (Google)
TensorFlow is an increasingly popular open source machine intelligence library that is especially well suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. Yufeng Guo and Amy Unruh offer an introduction to TensorFlow concepts and walk you through using CloudML to do distributed training and scalable serving of your models. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton North (Sponsored) Level: Intermediate
Arthur Juliani (Unity Technologies)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments has been deep reinforcement learning. Arthur Juliani covers RL from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Beekman Level: Intermediate
Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft), Patrick Buehler (Microsoft)
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.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Murray Hill E/W Level: Beginner
Yiheng Wang (Intel)
Yiheng Wang offers 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 demonstates how to develop with BigDL and shares some practical use cases. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Beginner
Laura Graesser (New York University)
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.
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9:05am9:20am Wednesday, June 28, 2017
Location: Grand Ballroom
Richard Socher (Salesforce)
AI presents a huge opportunity for businesses to personalize and improve customer experiences and improve efficiency, but the technical complexity of AI puts it out of reach for most companies. Richard Socher explains how Salesforce is doing the heavy lifting to deliver seamless and scalable AI to Salesforce customers. Read more.
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10:10am10:30am Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom
Josh Tenenbaum explains how to build machines that learn and think like people. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Implementing AI
Location: Gramercy East/West Level: Intermediate
Eric Greene (Wells Fargo Digital Innovation Labs)
Next-generation payment applications will soon be intelligent enough to automatically schedule payments for customers effortlessly. Eric Greene explores how Wells Fargo AI Labs developed accurate predictive models leveraging deep learning and terabytes of transaction history data that propose payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom West
Richard Socher (Salesforce)
Deep learning has made great progress in a variety of language tasks. However, there are still many practical and theoretical problems and limitations. Richard Socher shares some solutions. Read more.
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1:45pm2:25pm Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Guy Ernest (Amazon Web Services)
AWS is democratizing AI, helping you build deep learning systems in any scale, in any team size and skill, and for every use case. Guy Ernest discusses the state of deep learning, the tools that can take advantage of its power, and best practices for building successful businesses in the cloud, including data handling, models learning, deployment, and integration to other parts of the business. Read more.
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2:35pm3:15pm Wednesday, June 28, 2017
Interacting with AI
Location: Sutton South/Regent Parlor Level: Beginner
Yishay Carmiel (Spoken Communications)
There has been a quantum leap in the performance of conversational AI. From speech recognition to machine translation and language understanding, deep learning made its mark. However, scaling and productizing these breakthroughs remains a big challenge. Yishay Carmiel shares techniques and tips on how to take advantage of large datasets, accelerate trainings, and create an end-to-end product. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Interacting with AI
Location: Sutton South/Regent Parlor Level: Beginner
Mohamed Musbah (Maluuba Inc.)
AI research in comprehension, communication, and modeling human-like thinking skills is heralding the dawn of literate machines. Although there has been a lot of recent hype around bots, we’re only just beginning to see the potential for language understanding. Mohamed Musbah explores key research areas and explains how they will power new products and services in language understanding. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Yufeng Guo (Google)
Moving the heavy lifting of machine learning to the cloud is a great way to get large speed-ups. Yufeng Guo walks you through this process in detail so that you'll be ready to scale your own training and prediction services. Read more.
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4:50pm5:30pm Wednesday, June 28, 2017
Implementing AI
Location: Gramercy East/West Level: Intermediate
Thomas Wiecki (Quantopian)
Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. Thomas Wiecki demonstrates how to embed deep learning in the probabilistic programming framework PyMC3 to address uncertainty and nonstationarity. Read more.
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11:55am12:35pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Beginner
Soumith Chintala (Facebook)
Soumith Chintala discusses paradigm shifts in cutting-edge AI research and applications such as self-driving cars, robots, and game playing. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Beginner
Timothy Hazen (Microsoft)
Dramatic progress has been made in computer vision: deep neural networks (DNNs) trained on tens of millions of images can now recognize thousands of different object types. These DNNs can also be easily customized to new use cases. Timothy Hazen shares simple methods and tools that enable you to adapt Microsoft's start-of-the-art DNNs for use in your own computer vision solutions. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Reza Zadeh (Stanford | Matroid)
Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models and algorithms, model sharing between users, and serving infrastructure at scale. Reza Zadeh offers an overview o Matroid's pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Interacting with AI
Location: Gramercy East/West Level: Intermediate
Anusua Trivedi (Microsoft)
Anusua Trivedi outlines an AI bot that uses deep vision and advanced cognitive analytics to analyze medical imaging scans to help radiologists and emergency department physicians recognize hard-to-spot abnormalities and make better decisions. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Matt Zeiler (Clarifai)
AI-powered machine-learning technologies bring a higher and more complex level of technical debt to applications. Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors, and how to avoid or reduce technical debt related to machine learning. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Intermediate
Joseph Bradley (Databricks), Xiangrui Meng (Databricks)
Joseph Bradley and Xiangrui Meng share best practices for integrating popular deep learning libraries with Apache Spark, covering cluster setup, data ingest, configuring clusters, and monitoring jobs. Joseph and Xiangrui will demonstrate these techniques using Google’s TensorFlow library. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Murray Hill E/W Level: Intermediate
Pau Carré (Gilt)
Pau Carré explains how Gilt is reshaping the fashion industry by leveraging the power of deep learning and GPUs to automatically detect similar products and identify facets in dresses. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Sutton South/Regent Parlor Level: Beginner
Michael B. Henry (Mythic)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Grand Ballroom West Level: Intermediate
Yonghua Lin (IBM Research)
Yonghua Lin leads a deep dive into AI Vision, a deep learning system from IBM for image and video analysis in both edge and cloud environments, exploring its system design, performance optimization, and large-scale capability for training and inference. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Implementing AI
Location: Gramercy East/West Level: Intermediate
Jan Neumann (Comcast), Ferhan Ture (Comcast), Oliver Jojic (Comcast)
AI plays an essential role in creating the Comcast X1 entertainment experience and is how millions of its customers access their content on the TV. Jan Neumann, Ferhan Ture, and Oliver Jojic explain how AI enables Comcast to understand what you are looking for when you talk to the X1 voice remote and how Comcast scaled the voice interface to answer millions of voice queries every single night. Read more.