This work demonstrates the successful application of Deep Learning with phantom and human measurements for the reconstruction in Magnetic Resonance Fingerprinting (MRF). State-of-the-art MRF reconstruction yields quantitative maps of e.g. T1 and T2 by acquiring multiple undersampled images with various acquisition parameters, commonly referred to as fingerprints. Every measured fingerprint (per voxel) is compared with a dictionary of simulated fingerprints for possible parameter combinations. This time-consuming step can be replaced with a neural network, which directly predicts the parameters from a fingerprint. This was previously shown with simulated data. Here, we extend this approach to real measurements.