Deep Learning

Past Presentations

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)
[CANCELLED] [Cancelled] Maze Runner: Navigate Reinforcement Learning w/ Java

Lost and alone, our abandoned Java robot is forced to find its way through a series of rooms that all look the same. How can we help it? What algorithms can we use?   Reinforcement learning can help us, using one easy* algorithm and a lot of repetition. This talk will focus on getting the...

Katharine Beaumont Software developer
Tools to Put Deep Learning Models in Production

While there are a lot of machine learning frameworks and libraries available, putting the models in production at large scale is still a challenge. I’d like to talk about how we took on the challenge of supporting the data scientists with their efforts by making it easy to put their models in...

Sahil Dua Developer at Booking.com; Open Source Contributor in DuckDuckGo, GitHub and Pandas
Machine Intelligence at Google Scale

The biggest challenge of Deep Learning technology is the scalability. As long as using single GPU server, you have to wait for hours or days to get the result of your work. This doesn't scale for production service, so you need distributed training on the cloud eventually, or take advantage of...

Guillaume Laforge Developer Advocate at Google Cloud and PMC Chair for Apache Groovy

Interviews

Jessica Yung Machine Learning blogger and entrepreneur, Self-Driving Car Engineer Scholar @nvidia

Understanding Deep Learning

Tell me a bit about your experience with deep learning.

I frequently use deep learning for a range of different things, very often with time series. Previously I worked in finance. We tried to predict different kinds of stock prices, bond prices and economic indicators. I also worked with self-driving cars. The inspiration for this talk was really very much based in what I was doing because...

Read Full Interview
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