Deep Learning
Past Presentations
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...
Understanding Deep Learning
No matter what your role is, it is really important to have some understanding of the models you’re working with. In last year's keynote, Rob Harrop talked about the importance of intuition in machine learning. This is a step towards that. You might already be using neural networks. How can...
Visual Intro to Machine Learning and Deep Learning
Break into machine learning with this gentle and intuitive journey through central concepts in machine learning -- from the most basic models up to the latest cutting edge deep learning models. This highly visual presentation will give you the mental map of ML prediction models and how...
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.
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...
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...
Interviews
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...
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