Deep Learning
Past Presentations
[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...
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...
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.
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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