Dynamic Intepretation of Random Forests Predictions

Notes on understanding why Random Forests makes its decisions. Understanding Random Forests   A good and visual explanation of how Random Forests works. Model Feature Importances Feature importances can be taken from Scikit-learn and Spark MLLib implementations after training. However, this explains features as a whole based on the training dataset. i.e. We are still …

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How Deep is your… Neural Network? How deep should it be?

How many hidden layers? How deep should your neural network be? How large or deep a fully-connected neural network can or should be? All good questions, here we explore some answers. This book’s chapter takes the cake for how large or deep a fully-connected neural network can or should be: At present day, it looks …

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Machines – More creative than humans?

Being creative or artistic has long been the sole domain of humans. What if machines are able to be as creative? What if machines get better than humans at that? Allow me to show you some contemporary developments in Artificial Intelligence that might just challenge our assumptions in these four areas: Writing Art Music Problem-solving …

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Measuring Performance is about Measuring Relationships

What is the underlying focus of a KPI? It could be a telltale sign of the organization’s culture. The following post is a riveting and sometimes incriminating commentary on how relationship-based goals in an organization can prevent alienation of the people most important to it, namely: Employees Customers Partners Management A handy “test” is included …

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