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Presentation: Detecting Similar Id Documents Using Deep Learning

Track: Deep Learning Applications & Practices

Location: Cyril Magnin I

Duration: 2:20pm - 2:30pm

Day of week: Wednesday

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Abstract

Identity verification is the process in which we digitally confirm the legitimacy of a real world document. This process is necessary for many businesses to meet their compliance requirements and mitigate their fraud risk. A common form of fraud is the duplication and alteration of stolen documents across multiple user accounts. In this talk, we will discuss how Coinbase solves the problem of detecting similar identity documents using deep learning.

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: Burkay Gur

Risk Engineer @Coinbase

Find Burkay Gur at

Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches, including image recognition, NLP, predictions, & modeling.

  • Deep Learning in Practice

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe including use cases on machine translation, computer vision, & image recogition.

  • AI Meets the Physical World

    Where AI touches the physical world, think drones, ROS, NVidia, TPU and more.

  • Papers to Production: CS in the Real World

    Groundbreaking papers make real world impact.

  • Solving Software Engineering Problems with Machine Learning

    Anomaly detection, ML in IDE's, bayesian optimization for config. Machine Learning techniques for more effective software engineering.

  • Predictive Architectures in the Real World

    Case Study focused look at end to end predictive pipelines from places like Salesforce, Uber, Linkedin, & Netflix.