This talk introduces machine learning from a classification perspective. We present popular learning architectures and respective training algorithms that serve to design classifiers for a diversity of everyday problems. Emphasis will be given on shallow and deep feedforward neural networks, Recurrent Neural Networks and Hidden Markov models. On the application side, we will review state-of-the-art application programming interfaces (APIs), like Tensorflow, which are rapidly gaining popularity in the scientific community in the context of developing machine learning innovations.
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