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Название: Probing the Past with Data Analytics and AI
Автор: Marc Thuillard
Издательство: World Scientific Publishing
Год: 2025
Страниц: 260
Язык: английский
Формат: pdf (true)
Размер: 11.4 MB

This comprehensive compendium explains the technical challenges and opportunities behind the most recent and successful applications in Artificial Intelligence [AI] and data analytics. It focuses on applications that have the power to be adapted to many different fields and explains how AI can be implemented as an assistant in digital humanities. It also introduces new methods and applications in classification trees, networks, and Bayesian learning.

The useful reference text benefits professionals, academics, researchers, and graduate students in AI/Machine Learning, neural networks, and bioinformatics, and digital humanities.

The first chapter briefly introduces the most important classical classification techniques. The choice was set on methods that find broad applications in data analytics and AI further in the book. In this chapter, as in the rest of this book, we follow the strategy to explain and illustrate the main ideas behind the most advanced AI with minimal use of equations. The reader is furnished with many good references to enquire further.

The second chapter is on a family of Neural Networks described by the acronym “CNN”, which stands for convolutional neural networks. For years, neural networks steadily developed and found applications in different fields, some described in my book Wavelets in Soft Computing. Despite many applications, the real breakthrough in Artificial Intelligence came with the development of CNN and the realization of the capabilities of large neural networks using a massive amount of data for learning.

Chapter 3 is on the second major development in neural networks that led to Large Language Models (LLM), which are part of our daily lives nowadays. The chapter first explains the concept of word embedding: transforming a word into a series of numbers. The corresponding vectors characterize words in a dictionary. We explain the extension of the concept to the attention mechanism, the core component of LLM.

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