Titlebook: Deep Learning Foundations; Taeho Jo Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature

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arning algorithms for further analysis.Details how deep learThis book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervi

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Design for Six Sigma+Lean Toolsetles, each of which is labeled with its own target output, and the given learning algorithm are trained with them. Supervised learning algorithms are applied to classification and regression. This chapter is intended to review the supervised learning as a kind of swallow learning, before studying the deep learning.

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MERIT

https://doi.org/10.1007/978-3-030-69209-4connection from a neuron as an advanced model. In the feedback connection, a previous output value is used as an input. This chapter is intended to describe the recurrent neural networks and the variants with respect to the connection and the learning process.

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Manohar Mahato,Amarendra Kumar Dasariables which are given for restoring the input. The stacked version of multiple RBMs which is called belief networks is a kind of deep neural networks. This chapter is intended to describe the RBM, together with the stacked version with respect to its learning process.

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Supradip Das,Amarendra Kumar Das summary. The text summarization is viewed as mapping a text into a hidden text in implementing the textual deep learning. This section is intended to describe the text summarization with the view of implementing the textual deep learning.

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Book 2023zmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

CLEFT

Book 2023ep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms

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https://doi.org/10.1007/978-3-658-00828-4nced learning types than the deep learning: the kernel-based learning, the ensemble learning, and the semi-supervised learning. This chapter is intended to describe the deep learning conceptually for providing the introduction.