Overview
Deep learning has become one of the most used buzzwords in recent years. Businesses across the world are using advanced techniques to improve efficiency in sectors such as healthcare, automotive and even retail.
During this workshop you will discover how deep learning can be applied to your research through hands-on exercises in computer vision and natural language processing. You will start from scratch, training a deep learning model and learning tools and tricks to achieve accurate results.
The session will also showcase freely available, state-of-the-art pre-trained models to save you time and get your deep learning application up and running quickly.
Learning Outcomes
By the end of this workshop you will:
- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework
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.
The Python Beginner’s Guide at: https://wiki.python.org/moin/BeginnersGuide should satisfy these requirements.
Registering Your Interest
There are a limited number of places available for this course. Your application will be treated as an expression of interest and you are not guaranteed a place at the workshop.
When assigning places we will need to ensure that all of the N8 universities are fairly represented at the workshop.
As part of the application process, you will be asked to provide a brief explanation of how attending this workshop will benefit your research. You may find it useful to write this piece before attempting to register for the event.
About the trainer
Twin is a Research Software Engineer who completed his PhD at the University of Sheffield. He specialises in High-performance agent-based pedestrian simulation, Parallel computing using GPUs, Virtual reality and Deep learning. He currently provides consultancy, training and technical support for researchers on Deep learning and other GPU related software engineering problems.
Learning Outcomes
- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework