In the previous articles, we learned how neural networks perform estimations: a weighted sum is performed between the network inputs and its weights. Until now, the values of those weights were given to us by a mysterious external force. We took for granted that those are the values that produce

In the previous article we learned what a neural network is and how it performs predictions: the input is combined with knowledge (in the form of a weight value) to produce an output.

In practice, just one input and one weight are rarely of any use. Most systems in the

So, deep learning. Have you heard about it? If you work in the tech sector you probably have. Every week you see news about how people are using it to solve all sorts of interesting challenges around.

Because of all the interest (and sometimes raw hype) around deep learning, you

In a previous article, we learned about supervised learning: using labeled examples for creating models that solve a variety of tasks such as classification and regression.

There is another type of learning that doesn't require labeled examples: unsupervised learning. As it turns out, unlabeled data is much more common than