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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

A single artificial neuron – I – a primitive ANN for a classification problem

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 for relatively simple experiments. And the objects you work with will typically be described in high-dimensional variable spaces. The good news is that one can… Read More »A single artificial neuron – I – a primitive ANN for a classification problem