Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. This lecture will try to figure out what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review some of the most common architectures (CNN, LSTM, etc.) and their applications by following a hands-on approach. By the end of the lecture, attendants will be able to (i) describe how a neural network works and combine different types of layers and activation functions; (i) describe how these models can be applied in computer vision, text analytics, etc.; (iii) develop simple models in Tensorflow.