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
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… Read More »Surfaces of n-dimensional ellipsoids – I – quadratic form and matrix equation
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… Read More »The Meaning of Object Features in different ML-Contexts
This post requires Javascript to display formulas! When you start working with Artificial Neural Networks [ANNs] there are a lot of things you must get familiar with: Different types of networks and network layers, weights, signal propagation, loss, backward error propagation, gradient descent, regularization, normalization, tensors (arrays) …. In addition you may have to fight with complex layer structures even… Read More »A single artificial neuron – I – a primitive ANN for a classification problem