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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]. In this 2nd post I discuss the core elements of so called deep Residual Networks [ResNets]. ResNets have been published in multiple versions. The versions… 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 with standard CNNs. They have a tendency to eliminate some small scale patterns. Visually this leads to smoothing or smear-out effects. Due to an interference… Read More »ResNet basics – I – problems of standard CNNs

Surfaces of n-dimensional ellipsoids – I – quadratic form and matrix equation

This post requires Javascript to display formulas! Multidimensional ellipsoids are mathematically interesting figures per se. But there is a reason why they sometimes also appear in the context of Machine Learning experiments. One reason is that Multivariate Normal Distributions [MND] often describe the statistical distributions of properties which characterize natural objects we investigate by ML-methods. And the locations of constant probability density of MNDs are surfaces of multidimensional ellipsoids. MNDs and ellipsoids may therefore appear in data distributions in diverse variable spaces which we use… Read More »Surfaces of n-dimensional ellipsoids – I – quadratic form and matrix equation

The Meaning of Object Features in different ML-Contexts

When I gave a few introductory courses on basic Machine Learning [ML] algorithms in 2022, I sometimes ran into a discussion about “features“. The discussions were not only triggered by my personal definition, but also by some introductory books on ML the attendants had read. Across such textbooks, but even in a single book on ML the authors have a tendency to use the term “features” in different contexts of ML-algorithms and in particular Artificial Neural Networks [ANN]. Unfortunately, the meaning of the term is… Read More »The Meaning of Object Features in different ML-Contexts