Presentation: The Case for R for AI developers

Track: Hands-on Codelabs & Speakers Office Hours

Location: Mission

Duration: 9:00am - 9:10am

Day of week: Wednesday

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Abstract

While Python is a widely-used tool for AI development, in this talk I'll make the case for considering R as a platform for developing models for intelligent applications. Firstly, R provides a first-class experience working deep learning frameworks with its keras integration. Equally importantly, it provides the most comprehensive suite of statistical data analysis tools, which are extremely useful for many intelligent applications such as transfer learning. I'll give a few high-level examples in this talk, and we'll go into further detail in the accompanying interactive code lab.

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: David Smith

Cloud Developer Advocate @Microsoft

David is Cloud Developer Advocate for Microsoft, specializing in the topics of artificial intelligence and machine learning. Since 2009 he has been the editor of the Revolutions blog http://blog.revolutionanalytics.com where he writes regularly about applications of data science with a focus on the programming language "R", and is also a founding member of the R Consortium. He lives with his husband and two Jack Russell terriers in Chicago, where he also serves on the board of the Center on Halsted.

Find David Smith 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.