I began working on another video tutorial series with Python. In this series we look into a high-level, relatively unknown, Python library used for artificial neural networks. This library is called _neurolab_. 

I am still working on the [_Machine Learning with Scikit-Learn_](https://steemit.com/machine-learning/@cristi/machine-learning-with-scikit-learn-part-38) series, and I will be posting tutorials on both series in parallel. 

So, in this new series we're going to first go through some basic concepts of artificial neural networks and a little bit of history behind them. 

Then we are going to build a perceptron based classifier, possibly one of the simple ANN concepts. Then we are going to create a single layer neural network and a multi-layer neural network. This is just a few lines of code with _neurolab_. 

If you'd have to write all the code and the functions yourself, like the forward propagation, the back propagation, the activation functions, and so on, you'd definitely write some lengthy piece of code. This never hearts especially if you are a newcomer to the field because it's how you learn the nuts and bolts of neural networks. 

However, practitioners, many of whom already know what's behind the hood, find it more convenient to work with higher level libraries like _neurolab_, _TensorFlow_, _PyTorch_, _Keras_, and so on, because it takes a lot of burden out of their hands. 

Back to the main point, we are also going to go through some sequence models for time series problems in this tutorial series. So, if you want to follow along with the code, see the introductory video below and make sure to subscribe to know whenever a new tutorial is up:

<center><iframe width="560" height="315" src="https://www.youtube.com/embed/d1qVR_xbmn4" frameborder="0" allowfullscreen></iframe></center>
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[Cristi Vlad](http://cristivlad.com) Self-Experimenter and Author