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Introduction to Deep Learning on ShARC's DGX-1
Research Software Engineering Sheffield (RSES) and GPUComputing@Sheffield are pleased to announce that there will be a 2-part Deep Learning training course held at the University of Sheffield on February 16th and 23rd 2017 (from 13:00-17:00 on both days).
The course aims to introduce core concepts of deep learning and how it can be applied to your research in a practical way. The course will specifically look at the use of Caffe deep learning package on the DGX-1 deep learning supercomputer hosted in our new ShARC cluster. Through practical examples you will learn to:
- Implement convolution models for image classification
- Implement recurrent models for serial inputs and outputs such as text prediction
- Visually debugging your model by visualising their weights
- Use and refine existing pre-trained models to your needs
- Utilise multiple GPUs to accelerate the training of your models
This course is for Computer Science first and foremost. As such Computer Science staff and researchers will be given priority for this course. Additional places will be assigned on a first come first serve basis. Maximum of 20 places available.
An Iceberg account is required so if you don't already have one contact firstname.lastname@example.org to create an account. Also please send us your username after so we can add you to the ShARC's access list.
Familiarity with Linux, the use of command line, Python and basic understanding of neural network.
If you're not familiar with Nueral Networks please see Stephen Welch's Neural Networks Demystified to get a better understanding. Working through his code exercises may also be useful to you but is not essential for the course:
Short Videos (7 in total) - https://www.youtube.com/playlist?list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU
Accompanying Code - https://github.com/stephencwelch/Neural-Networks-Demystified
Suppliment material (not essential for the course)
See a neural network in action at the Neural Network Playground:
For deeper understanding on the subject of Machine Learning and Neural Networks, see Andrew Ng's excellent Machine Learning module on Coursera: