Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are deep learning architectures, inspired by the visual perception mechanism of animals based on receptive fields in the visual cortex (Gu et al., 2018). CNNs have been known for a couple of decades, e.g. LeChun et al. (LeCun et al., 1990) showed in 1989 that CNNs can be used to classify images of hand-written digits. Since the mid 2000s CNNs started to get more popular because the previously occurring problems like the lack of training data and computational resources became less relevant (Gu et al., 2018). This was on the one hand due to the improvement of the methods on the other hand due to the usage of more efficient hardware. Nowadays CNN architectures reach state-of-the-art performance in many image related tasks like image classification, which is the task of assigning the correct class to an image (or image patch) or semantic segmentation, where the goal is to assign the correct class to each pixel of the input image.
- [slides] Convolutional Neural Networks
- [theoretical-background] Optimization Using (Stochastic) Gradient Decent
-  Neural Networks Basics
- [slides] ML Deep Learning
- [slides] Neural Networks - Basics
- [theoretical-background] Optimization Methods