SESSION + Live Q&A
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 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.
Speaker
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...
Read moreSpeaker
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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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
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Deep Learning @Google Scale: Smart Reply in Inbox
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Anjuli Kannan
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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)
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