Machine Learning

How to use bnlearn to learn causal structures

This article on causal machine learning covers a practical example of how to learn structural causal models (SCM) directly from data. We will use bnlearn, which is an open-source library for learning the graphical structure of Bayesian networks in Python. Check out my Github repo for additional code examples. For other frameworks, checkout my page …

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Symbiosen mellem mennesker og AI vil kunne transformere mennesket til en rationel organisme (jvf. Daniel Kahneman som har påvist at mennesket for sig selv ikke er en rationel organisme). Hvordan det? Vores minutiøse adfærd bliver i stigende grad sporet i alle livets væsentlige forhold. Kunstig intelligens bliver bedre og bedre til at skønne om vi …

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PyBrain quickstart and beyond

After pip install bybrain, the PyBrain the quick start essentially goes as follows: from import buildNetwork from pybrain.structure import TanhLayer from pybrain.datasets import SupervisedDataSet from pybrain.supervised.trainers import BackpropTrainer   # Create a neural network with two inputs, three hidden, and one output net = buildNetwork(2, 3, 1, bias=True, hiddenclass=TanhLayer)   # Create a dataset …

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