Recurrent Neural Networks are a powerful class of statistical models which allow neural networks to deal with sequential data. They have recently become a powerful component within the Deep Learning community for achieving state of the art performance on tasks such as captioning, translation, and summarization. This talk will provide a brief introduction to the terminology of recurrent neural networks and then focus on how to create and train them from Python. I will show non-trivial network implementations using the most popular Python deep learning libraries (Keras, Lasagne, Blocks, Chainer) and compare their performance and extensibility.