Deep Learning has taken a prominent place for tasks involving predictive modelling and pattern recognition. Deep Learning with its auto feature extraction and feed-forward methods gives the confidence to extract low-level features in order to identify high-level identities in big data applications. However, deep neural networks have drawbacks, which include many hyperparameters tuning together, slow convergence in smaller datasets and issues explaining why a particular decision was been made. While traditional machine learning algorithms can address these drawbacks, they are not typically capable of achieving the performance levels registered by deep neural networks. To improve performance, ensemble methods are used to combine multiple base learners. Read More

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