In recent years large advances have been made in the field of machine learning, driven by novel deep learning methods. Deep learning is a research field that focusses on creating neural networks. This field has seen a rapid advance due to an increase in computational power, availability of large amounts of data and a wide variety of novel methods that allows for more efficient training of neural networks. Deep learning has been applied in various fields to solve many different tasks. Effective training of these neural networks requires selecting the right data, network architecture and learning method. However, thorough understanding of the task for which the neural network is trained is needed to adhere to these requirements. This thesis will illustrate that deep learning methods can effectively be applied to perception tasks by thorough understanding of the task.
|Qualification||Doctor of Philosophy|
|Award date||4 Jan 2021|
|Publication status||Published - 2021|