Titlebook: Machine Learning with Python; Theory and Implement Amin Zollanvari Textbook 2023 The Editor(s) (if applicable) and The Author(s), under exc

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Ensemble Learning,element is induced in the splitting strategy. This randomization often leads to improvement over bagged trees. In pasting, we randomly pick modest-size subsets of a large training data, train a predictive model on each, and aggregate the predictions. In boosting a sequence of weak models are trained

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Assembling Various Learning Steps, with resampling evaluation rules. To keep discussion succinct, we use feature selection and cross-validation as typical representatives of the composite process and a resampling evaluation rule, respectively. We then describe appropriate implementation of

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Deep Learning with Keras-TensorFlow,n this regard, we use multi-layer perceptrons as a typical ANN and postpone other architectures to later chapters. In terms of software, we switch to Keras with TensorFlow backend as they are welloptimized for training and tuning various forms of ANN and support various forms of hardware including C

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Recurrent Neural Networks,its input observations and weights. Therefore, in contrast with other common architectures used in deep learning, RNN is capable of learning sequential dependencies extended over time. As a result, it has been extensively used for applications involving analyzing sequential data such as time-series,