Introduction to PyTorch

Name: Introduction to PyTorch
Start: 2022-05-10T12:00:00-04:00
End: 2022-05-10T13:00:00-04:00

Hosted By

Beryl K. and Reshama S.

Details

## Link to join the webinar here:

https://www.bigmarker.com/neo4j/Data-Umbrella-Webinar

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Video Recording
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This event will be recorded and placed on our YouTube. We usually have it up within 24 hours of the event. Subscribe to our YouTube and set your notifications:
https://www.youtube.com/c/DataUmbrella/

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Time
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9am PT / 12pm ET / 7pm EAT / 9:30 PM IST

## Speaker

Sebastian Raschka and Adrian Wälchli

## Talk Level

Intermediate

## Pre-reqs

Beginner knowledge in Python and Machine Learning.

## Prep Work

None

## Resources

Will be provided.

## Event

This talk will introduce attendees to using PyTorch for deep learning. We will start by covering PyTorch from the ground up and learn how it can be both powerful and convenient. At times, Machine learning models can become so large that they can't be trained on a notebook anymore. Being able to take advantage AI-optimized accelerators such as GPU or TPU and scaling the training of models to hundreds of these devices is essential to the researcher and data scientist.
However, adding support for one or several of these in the source code can be complex, time consuming and error-prone. What starts as a fun research project ends up being an engineering problem with hard to debug code. This talk will introduce LightningLite, an open source library that removes this burden completely. You will learn how you can accelerate your PyTorch training script in just under ten lines of code to take advantage of multi-GPU, TPU, multi-node, mixed-precision training and more.

## Speaker

Sebastian is a machine learning and AI researcher with a strong passion for education. As Lead AI Educator at Grid.ai, he is excited about making AI & deep learning more accessible and teaching people how to utilize AI & deep learning at scale. Sebastian is also an Assistant Professor of Statistics at the University of Wisconsin-Madison and the author of the Machine Learning with PyTorch and Scikit-Learn book.

Adrian is a research engineer at Grid.ai developing and maintaining PyTorch Lightning, a library for researchers and deep learning practitioners built on top of PyTorch, minus the boilerplate. Previously, Adrian graduated with a BSc and MSc in Computer Science at University of Bern, Switzerland. Before joining Grid in 2021, he worked for three years as a PhD student in the Computer Vision Group at the Institute of Computer Science, University of Bern, under the supervision of Prof. Dr. Paolo Favaro. The topics he is passionate about are machine learning at scale, computer vision, computer graphics and mathematics.

LinkedIn: https://www.linkedin.com/in/sebastianraschka/
Twitter: https://twitter.com/rasbt
GitHub: https://github.com/rasbt