Читать книгу Informatics and Machine Learning. From Martingales to Metaheuristics онлайн

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For translation we have a BDwBF problem. The feature set is so complex the best approach is NN Deep Learning where we assume no knowledge of the features but rediscover/capture those features in compressed feature groups that are identified in NN learning process at the first layer of the NN architecture. This begins a process of tuning over NN architectures to arrive at a compressive feature acquisitiuon with strong classification performance (or translation accuracy, in this example). This learning approach began seeing widespread application in 2006 and is now the core method for handling the Big Feature Set (BFS) problem. The BFS problem may or may not exist at the initial acquisition (“front‐end”) of your signal processing chain. NN Deep Learning to solve the BFS problem will be described in detail in ssss1, where examples using a Python/TensorFlow application to translation will be given. In the NN Deep Learning approach, the features are not implicitly resolvable, so improvements are initially brute force (even bigger data) since an engineering cycle refinement would involve the enormous parallel task of explicitly resolving the feature data to know what to refine.