A Vectorized Version of the Mighty Logistic Regressor
Neural nets, with their flexible data representation capable of approximate any arbitrary function, are in a new renaissance with the development of Deep Leaners. These stack many layers (typically dozens of quasi-independent layers) of traditional neurons to achieve something very remarkable: self detection of important features from input data. The application of Deep Learning is not restricted to raw classification or regression; these family of techniques are applied to much broader fields such as machine vision and speech recognition. Recent advances went much further and combined a Deep Learning architecture with a Reinforcement Learning algorithm generating a computer program capable of beating classic Atari video games without being explicitly programmed for this task, scoring as a top human player. It is not surprising, therefore, that they appear in the last Nature’s cover (vol. 518, Num. 7540, pp. 456-568) . But how do they work? First, one must take a close look to the hum