There has been a lot of buzz surrounding neural networks in recent years with achievements made throughout various domains such as computer vision and natural language processing. But what on Earth are they? In these lecture notes and corresponding lectures we will explore, investigate and dissect some of the ideas behind neural networks.
Across many projects I’ve worked with, I’ve noticed some simple and effective steps for training neural networks to reduce frustration. I would recommend to follow these tips and tricks when training deep neural network models.
There is increasing interest in neuro-symbolic methods that combine recent advances in deep learning with symbolic methods of Artifical Intelligence. Yet, at the heart of their integration lies an intriguing puzzle.
Thanks to recent libraries such as TensorFlow and PyTorch, deep learning has become so accessible that it now can be in anyone’s toolbox. But at what cost does these libraries reduce the entry level for advanced methods?
A very quick introduction to Keras and common architecture types with short examples. The slides include code snippets and tips on training neural networks.