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

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.