AI Training Series - Fundamentals of Deep Learning - powered by NVIDIA

Contents

In this intensive 2-day course, packed with lectures about Deep Learning and AI, you will learn:

• to train and deploy deep neural networks to solve computer vision problems,
• the fundamentals of machine learning for working with texts,
• to effectively parallelize training of deep neural networks on single and Multi-GPUs

The lectures are interleaved with many demos and hands-on sessions using Jupyter Notebooks. Students will have access to a fully configured GPU-accelerated workstation in the AWS cloud.

The workshop is co-organised by LRZ and NVIDIA Deep Learning Institute (DLI). Material developed by NVIDIA is supplemented with vendor-neutral material developed by LRZ.

1st day: Fundamentals of Deep Learning (10:00-17:00 CEST) 

Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.

During this day, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:

• Implement common deep learning workflows, such as image classification and object detection
• Experiment with data, training parameters, network structure, and other strategies to increase performance and capability
• Deploy your neural networks to start solving real-world problems

Upon completion, you’ll be able to start solving problems on your own with deep learning.

2nd day: Fundamentals of Deep Learning for Multi-GPUs (10:00-17:00 CEST) 

The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU or even years for larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

On this day we will teach you how to use multiple GPUs to train neural networks. You'll learn:

• Approaches to multi-GPUs training

• Algorithmic and engineering challenges to large-scale training

• Key techniques used to overcome the challenges mentioned above

Upon completion, you'll be able to effectively parallelize training of deep neural networks.

 

Prerequisites

An understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays; familiarity with Pandas data structures; and an understanding of how to compute a regression line.

Important information

After you are accepted, please create an account under courses.nvidia.com/join.

Ensure your laptop / PC will run smoothly by going to http://websocketstest.com/ Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80).If there are issues with WebSockets, try updating your browser.

Hands-On

The lectures are interleaved with many hands-on sessions partly using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the AWS cloud and on LRZ AI systems. Demos on how to access and operate the LRZ AI system will be showcased. In addition, the parallelisation of the training of a ML model will be also demonstrated. 

Language

English

Lecturers

PD Dr. Juan Durillo Barrionuevo (LRZ, NVIDIA certified University Ambassador)

Prices and Eligibility

The course is open and free of charge for academic participants from Germany.

Registration

Please register with your official e-mail address to prove your affiliation.

Withdrawal Policy

See Withdrawal

Legal Notices

For registration for LRZ courses and workshops we use the service edoobox from Etzensperger Informatik AG (www.edoobox.com). Etzensperger Informatik AG acts as processor and we have concluded a Data Processing Agreement with them.

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Online Course	AI Training Series - Fundamentals of Deep Learning - powered by NVIDIA
Number	hdta3s24
Available places	0
Date	02.05.2024 – 03.05.2024
Price	EUR 0.00
Location	ONLINE
Room
Registration deadline	25.04.2024 23:59
E-mail	education@lrz.de