Speaker: Martin Gorner

Parallel processing and machine learning @Google

Martin is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech with a major in computer vision, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning (Dataflow and Tensorflow).

Find Martin Gorner at

Workshop : TensorFlow without a PhD: Deep Learning Guided Codelabs

Talk : TensorFlow without a PhD Codelab Speaker Office Hours

Talk : Tooling & Setup for My Neural Network

Talk : Tensorflow without a PhD Codelab Speaker Office Hours

Talk : Tensorflow Recurrent Neural Network Codelab Office Hours

Talk : Google Dataflow Codelab Speaker Office Hours

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.