Machine learning and deep learning have seen a research boom in the latest years. Deep learning in particular has significantly improved some long standing problems in machine learning, such as image recognition, speech recognition, and natural language processing (NLP). Deep learning can be understood as automatic feature extraction, rather than engineering features for shallow neural networks or other machine learning algorithms. In deep learning, the first layers extract and create useful features for the subsequent layers and final predictions. As such, deep learning can use formats that are more "raw" and yet reach similar or better performance than approaches that use engineered approaches. On the downside, deep learning needs enough data to learn the complex, often nonlinear, transformations necessary to convert the raw format into useful features for predictive modelling.