Track: Modern Learning Systems

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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.

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 new technologies. He has a PhD in astrophysics from Oxford.
10:35am - 11:25am

by Micha Gorelick
Research Engineer @FastForwardLabs, Keras Contributor

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 and deploy the model. In the process, he'll place Keras in context in the deep learning framework ecosystem, that includes Tensorflow, MXNet and Theano.

11:50am - 12:40pm

by Micha Gorelick
Research Engineer @FastForwardLabs, Keras Contributor

by Mike Lee Williams
Director of Research @FastForwardLabs

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 be working with using the following three commands:

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1:40pm - 2:30pm

by Stephen Whitworth
Co-founder and Machine Learning Engineer @Ravelin

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 testing models, instrumenting their behaviour and the ability to introspect and debug incorrect predictions. Wouldn't it be nice to have the best of the software engineering and machine learning...

2:55pm - 3:45pm

by Dr. Viral Shah
Co-Founder and CEO of Julia Computing and a Co-Creator of the Julia language

by Dr. Simon Byrne
Quantitative Software Developer @JuliaComputing

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 showcase a some of the cutting edge machine learning packages that available in the Julia ecosystem, as well as the tools to deploy these models at large scale.

4:10pm - 5:00pm

by Scott Le Grand
Deep Learning Engineer @Teza (ex-Amazon, ex-NVidia)

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 and that they handled sparse datasets incredibly inefficiently. DSSTNE provides nearly free sparse input layers for neural networks and stores such data in a CSR-like format that allowed us to train...

5:25pm - 6:15pm

by Anjuli Kannan
Software Engineer @GoogleBrain

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.

Tracks

Monday, 6 March

Tuesday, 7 March

Wednesday, 8 March