Presentation: NVIDIA Jetson

Track: AI Meets the Physical World

Location: Cyril Magnin II

Duration: 12:50pm - 1:00pm

Day of week: Tuesday

Share this on:

Abstract

  • Quick Overview of the TX2 hardware and TX2 Dev Kit
  • Software that runs on the TX2 Devkit

Examples: Supervised DL for Classification, Segmentation from the camera and RL projects

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: Dana Sheahen

Robotics @Udacity

Dana currently works as a Curriculum Lead for Udacity leading their Robotics Nanodegree program content development in Mountain View, California. As a BSEE with an emphasis in software, she previously worked in a number of industry areas including automotive systems, medical instrumentation, and environmental data analysis. More recently, Dana obtained her Master’s in Computer Science from Georgia Tech with a specialty in Interactive Intelligence, and has focused on AI projects and AI education ever since.

Find Dana Sheahen at

Proposed Tracks

  • Real-World Data Engineering

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

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.