Track Overview
Modern Learning Systems
Breakthroughs in fundamental algorithms, hardware and tooling mean that modern learning systems look very different to those deployed just a few years ago. In this session we'll cover the practical, real world use of the latest machine learning technologies in production environments.
We'll learn about the technical details of deep learning and artificial intelligence products from the people who built and deployed them in extremely large scale, high profile systems. We'll hear about the latest libraries and toolkits, which make prototyping and productionizing new ideas easier and quicker. And we'll learn about how we can make use best practices from software engineering to make this historically fragile and costly area of software development more rigorous and reliable.
From this track
Building Robust Machine Learning Systems
Machine learning is powering huge advances in products that we know and love. As a result, ever growing parts of the systems we build are changing from the deterministic to the probabilistic. The accuracy of machine learning applications can quickly deteriorate in the wild without strategies for...
Stephen Whitworth
Co-founder and Machine Learning Engineer @Ravelin
Deep Learning @Google Scale: Smart Reply in Inbox
Anjuli will describe the algorithmic, scaling and deployment considerations involved in an extremely prominent application of cutting-edge deep learning in a user-facing product: the Smart Reply feature of Google Inbox.
Anjuli Kannan
Software Engineer @GoogleBrain
Products And Prototypes With Keras
In this talk Micha will show how to build a working product with Keras, a high level deep learning framework. He'll start by explaining deep learning at a conceptual level, before describing the product requirements. He'll then show code and discuss design decisions that demonstrate how to train...
Micha Gorelick
Research Engineer @FastForwardLabs, Keras Contributor
DSSTNE: Deep Learning at Scale
DSSTNE (Deep Sparse Scalable Tensor Network Engine) is a deep learning framework for working with large sparse data sets. It arose out of research into the use of deep learning for product recommendations after we realized existing frameworks were limited to a single GPU or data-parallel scaling...
Scott Le Grand
Deep Learning Engineer @Teza (ex-Amazon, ex-NVidia)
Julia: A Modern Language For Modern ML
Julia is a modern high-performance, dynamic language for technical computing, with many features which make it ideal for machine learning, including just-in-time (JIT) compilation, multiple dispatch, metaprogramming and easy to use parallelism. This talk will demonstrate these features, and...
Dr. Viral Shah
Co-Founder and CEO of Julia Computing and a Co-Creator of the Julia language
Dr. Simon Byrne
Quantitative Software Developer @JuliaComputing
Mini Workshop: Hands-on Deep Learning
In this interactive workshop, Micha Gorelick will lead you through modification an existing deep learning product implemented in Keras. If you plan to run the code, please come with a well-charged laptop battery! And if you get the chance, please also download the python packages and data we'll...
Micha Gorelick
Research Engineer @FastForwardLabs, Keras Contributor
Mike Lee Williams
Director of Research @FastForwardLabs
Speakers from this track
Stephen Whitworth
Co-founder and Machine Learning Engineer @Ravelin
Stephen is a co-founder and machine learning engineer at Ravelin, helping fight fraudsters online. He previously worked at Hailo, where he built data products and simulations to understand how people move around cities. He also started golearn, one of the most popular machine learning libraries...
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Anjuli Kannan
Software Engineer @GoogleBrain
Anjuli Kannan is a senior software engineer at Google. She is a member of the Brain Team, which works to advance the field of machine intelligence through a combination of basic research, software (TensorFlow), and applications that improve people's lives. Anjuli is especially interested...
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Micha Gorelick
Research Engineer @FastForwardLabs, Keras Contributor
Research engineer at Fast Forward Labs, Keras contributor. Previously at bit.ly.
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Scott Le Grand
Deep Learning Engineer @Teza (ex-Amazon, ex-NVidia)
Scott is a senior scientist at Teza Technologies. He spent four years at Amazon where he was the lead author of DSSTNE, the Deep Scalable Sparse Tensor Network. Before that he spent ten years at NVidia, doing work that resulted in 14 GPU-related patents.
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Dr. Viral Shah
Co-Founder and CEO of Julia Computing and a Co-Creator of the Julia language
Dr. Viral B. Shah is a Co-founder and CEO of Julia Computing and a co-creator of the Julia language. The Julia user base is now over 200,000 users. Viral has a Ph.D. in computational sciences from UC Santa Barbara, where his thesis was on interactive supercomputing. The technology developed in...
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Dr. Simon Byrne
Quantitative Software Developer @JuliaComputing
Dr Simon Byrne is a quantitative software developer at Julia Computing, where he implements cutting edge numerical routines for statistical and financial models. Simon has a PhD in statistics from the University of Cambridge, and has extensive experience in computational statistics and machine...
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Mike Lee Williams
Director of Research @FastForwardLabs
Mike Lee Williams is Director of Research at Fast Forward Labs, an applied machine intelligence lab in New York City. He builds prototypes that bring the latest ideas in machine learning and AI to life, and works with Fast Forward Labs's clients to help them understand how to make use of these...
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Track Host
Mike Lee Williams
Director of Research @FastForwardLabs
Mike Lee Williams is Director of Research at Fast Forward Labs, an applied machine intelligence lab in New York City. He builds prototypes that bring the latest ideas in machine learning and AI to life, and works with Fast Forward Labs's clients to help them understand how to make use of these...
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