The commonly called deep learning or hierarchical learning is now a popular trend in machine learning. Recently during the Swiss Analytics Meeting Prof. Dr. Sven F. Crone presented how we can use deep learning in the industry in a forecasting perspective (beer forecasting for manufacturing, lettuce forecasting in retail outlets, container forecasts). Deep learning has a variety of applications as for example image and handwritten character recognition. It analyses a picture and will be able to conclude if it is a dog, a human or something else. After a learning process, deep learning first understands your handwriting and then can read and interpret a draft paper you have quickly written. But briefly what is exactly deep learning? In the artificial intelligence process, deep learning plays an important role. It is considered as a method of machine learning and roughly speaking means neural networks. More precisely artificial neural networks are intended to simulate the behaviour of biological systems composed of multiple layers of nodes (or computational units), usually interconnected in feed-forward way. Each node in one layer has directed connections to the nodes of the subsequent layer. Feed-forward neural networks can be considered as a type of non-linear predictive models that takes inputs (very often huge amount of both labelled and unlabelled data), transforms and weights these through plenty of hidden layers to produce a set of outputs (predictions). The use of a sequence of layers, organised in deep or hierarchical levels, explains the term of « deep learning ». Each layer receives as input the information contained in the previous layer, transforms it to the following layer and of course complete and improve it.
In this excellent tutorial video presentation below, Magnus Erik Hvass Pedersen demonstrates the basic workflow of using TensorFlow with a simple linear model. After loading the so-called MNIST data-set with images of hand-written digits, he defines and optimizes a simple mathematical model in TensorFlow. The results are then plotted and discussed. You can find the Jupyter notebook for the demo HERE. You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor. It also helps if you have a basic understanding of Machine Learning and classification.