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Conv2d

ResNet basics – II – ResNet V2 architecture

In the 1st post of this series ResNet basics – I – problems of standard CNNs I gave an overview over building blocks of standard Convolutional Neural Networks [CNN]. I also briefly discussed some problems that come up when we try to build really deep networks with multiple stacks of convolutional layers [e.g. Conv1D- or Conv2D-layers of the Keras framework].… Read More »ResNet basics – II – ResNet V2 architecture

ResNet basics – I – problems of standard CNNs

Convolutional Neural Networks [CNNs] do a good job regarding the analysis of image or video data. They extract correlation patterns hidden in the numeric data of our media objects, e.g. images or videos. Thereby, they get an indirect access to (visual) properties of displayed physical objects – like e.g. industry tools, vehicles, human faces, …. But there are also problems… Read More »ResNet basics – I – problems of standard CNNs