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Recent advances in deep neural networks coupled with an increasing amount and complexity of scientific data collected in a wide array of domains provide many exciting opportunities for deep learning applications in scientific settings. Furthermore, libraries such as Google’s Tensorflow python library have made building deep learning models more accessible through common tools, freely available to researchers.
In this course, we’ll introduce some basic neural network and deep learning theory and give participants practical experience in some popular deep learning models and techniques.
The course will be delivered online through blackboard collaborate. There will be a significant practical element to the course and we will be working online in Google Colab notebooks.
Familiarity with python programming basics.
We are delighted to be able to make free at point of use training available to the research community, to enable better software and more open, reproducible research. However, free at point of use training is not free. The cost of a course can easily run to thousands of pounds, if preparation costs are taken into account.
If you sign up for a course, please make sure you either attend or cancel your booking. Bookings can usually be cancelled using eventbrite.com or, failing that, by emailing email@example.com.
Running courses that are not fully attended wastes our funding (which is provided by taxpayers, charities and students, amongst others) and reduces our collective capacity to improve research outputs and researcher experiences.
Persistent failure to attend booked courses might result in you being excluded from future training opportunities.
For queries relating to collaborating with the RSE team on projects: firstname.lastname@example.org
Information and access to JADE II and Bede.
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Queries regarding free research computing support/guidance should be raised via our Code clinic or directed to the University IT helpdesk.