Both Deep Learning and Symbolic Regression can be used to solve machine learning problems, but the approaches are entirely different. Here we will explain which problems are most suitable for each method.

Read MoreWhen it comes to AI, neural networks are the first method that comes to mind. Despite their impressive performance on a number of applications, we want to argue that they are not necessarily a good general-purpose machine learning method.

Read MoreIn this article, we will discuss a very basic question regarding machine learning: is every model a black box? Certainly most methods seem to be, but as we will see, there are very interesting exceptions to this.

Read MoreIn this article, we will see some alternatives to neural networks that can be used to solve the same types of machine learning tasks that they do.

Read MoreIf you are interested in solving AI problems and would like an easy-to-use desktop software that yields state-of-the-art results, you might like TuringBot. In this article, we will show you how it can be used to easily solve classification and regression problems, and explain the methodology that it uses, which is called symbolic regression.

Read MoreGiven a sequence of numbers, finding an explicit mathematical formula that computes the nth term of the sequence can be challenging, except in very special cases like arithmetic and geometric sequences.

Read MoreMany machine learning methods are presently available, including for instance neural networks, random forests and support vector machines. In this article, we will talk about a very unexplored algorithm called symbolic regression, and will show how it can be used to solve machine learning problems in a very transparent and explicit way.

Read MoreFinding mathematical formulas from data is an extremely useful machine learning task. A formula is the most compressed representation of a table, allowing large amounts of data to be compressed into something simple, while also making explicit the relationship that exists between the different variables.

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