## Deep Learning vs Symbolic Regression: What’s The Difference?

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.

## Machine learning black box models: some alternatives

In 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.

## Neural networks: what are the alternatives?

In 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.

## A free AI software for PC

If 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.

## How to find a formula for the nth term of a sequence

Given 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.

## Symbolic regression tutorial with TuringBot

In this tutorial, we are going to show how you can find a formula from your data using the symbolic regression software TuringBot. It is a desktop software that runs on both Windows and Linux, and as you will see the usage is very simple.

## Machine learning with symbolic regression

Many 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.