Presentation: Deep Learning for Language Understanding (at Google Scale)

Track: Handling Sequential Data Like an Expert / ML Applied to Operations

Location: Cyril Magnin II

Duration: 10:35am - 10:45am

Day of week: Wednesday

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Abstract

Written and spoken language are everywhere: customer reviews, online forums, chat applications, just to name a few. How can we use deep learning to extract meaning from these rich sources of data?

In this talk we'll take an in-depth look at the deep learning techniques for language understanding that are behind Google-scale applications like Gmail Smart Reply and Google Translate. We'll cover both the high level intuition for these techniques, and practical pointers for getting started in Tensorflow.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Anjuli Kannan

Software Engineer @GoogleBrain

Anjuli Kannan is a senior software engineer at Google. She is a member of the Brain Team, which works to advance the field of machine intelligence through a combination of basic research, software (TensorFlow), and applications that improve people's lives. Anjuli is especially interested applications of machine learning to problems in natural language understanding. Recently she was a core member of the team that brought the Smart Reply feature to Inbox by Gmail. Launched in 2015, Smart Reply was the first Google-scale application to effectively apply recurrent neural networks in language understanding, as well as the first to leverage Google's open-source TensorFlow.

Find Anjuli Kannan at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.