Deep Learning Foundations

This 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 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 into deep learning algorithms. The book's third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann 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.

Provides a conceptual understanding of deep learning algorithms Presents ways of modifying existing machine learning algorithms into deep learning algorithms for further analysis Details how deep learning can solve problems such as classification, regression, and clustering

Autorentext

The author, Taeho Jo, is president and founder of Alpha Lab AI. He received Bachelor, Master, and PhD degree, from Korea University in 1994, from Pohang University in 1997, and from University of Ottawa, 2006. As his research achievements, since 1996, he has published more than 200 research papers, and his research interests are text mining, machine learning, neural networks, and information retrieval. He has awarded three times in the world-wide biography, "Marquis who's who in the World", in 2016, 2018, and 2019, and is granted the noble title, "Duke" from United Kingdom, in August 2018. He previously published two books, titled, "Text Mining: Concept, Implementation, and Big Data Challenge" and titled "Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning".

Inhalt
Introduction.- Part I. Foundation.- Supervised Learning.- Unsupervised Learning.- Ensemble Learning.- Part II. Deep Machine Learning.- Deep K Nearest Neighbor.- Deep Probabilistic Learning.- Deep Decision Tree.- Deep SVM.- Part III. Deep Neural Networks.- Multiple Layer Perceptron.- Recurrent Networks.- Restricted Boltzmann Machine.- Convolutionary Neural Networks.- Part IV. Textual Deep Learning.- Index Expansion.- Text Summarization.- Textual Deep Operations.- Convolutionary Text Classifier.- Conclusion.<p