Presentation: PyTorch by Example

Track: Deep Learning Applications & Practices

Location: Cyril Magnin I

Duration: 9:00am - 9:10am

Day of week: Wednesday

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Abstract

An introduction to PyTorch, a comparison to other frameworks and how to build neural networks with it.

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: Jendrik Joerdening

Data Scientist @Aurubis

Jendrik is a Data Scientist at Aurubis.He is working on Data Science and Deep Learning in the field of Industry 4.0.After studying Physics and Space Science and Technology, where he determined the communication strategies for the Mars 2020 rover, he worked at Akka Germany as a Data Scientist and now moved to Aurubis staying in the same field. At the same time he takes part in the Udacity Self-Driving Car Nanodegree. In this context, he participated with a group of other Udacity student in the Self-Racing Cars event at the Thunderhill race-track in California. There the group of students taught a car to drive around every turn of the race track autonomously.

Find Jendrik Joerdening 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.