Open Source Deep Learning Frameworks and Visual Analytics

Deep Learning gets more and more traction. It basically focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game changer in analytics, when to use it, and how Visual Analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist.

What is Deep Learning and Artificial Neural Networks?

Deep Learning is the modern buzzword for artificial neural networks, one of many concepts and algorithms in machine learning to build analytics models. A neural network works similar to what we know from a human brain: You get non-linear interactions as input and transfer them to output. Neural networks leverage continuous learning and increasing knowledge in computational nodes between input and output. A neural network is a supervised algorithm in most cases, which uses historical data sets to learn correlations to predict outputs of future events, e.g. for cross selling or fraud detection. Unsupervised neural networks can be used to find new patterns and anomalies. In some cases, it makes sense to combine supervised and unsupervised algorithms.

Neural Networks are used in research for many decades and includes various sophisticated concepts like Recurrent Neural Network (RNN), Convolutional Neural

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