Regularização, aprendizagem profunda e interdisciplinaridade em problemas inversos mal-postos
It provides an interdisciplinary overview of regularization for researchers and practitioners in related fields, but is incremental as it synthesizes existing knowledge.
The book discusses ill-posed problems and how regularization methods solve them, exploring the origins and future of regularization across fields like inverse problems, statistics, machine learning, and deep learning.
In this book, written in Portuguese, we discuss what ill-posed problems are and how the regularization method is used to solve them. In the form of questions and answers, we reflect on the origins and future of regularization, relating the similarities and differences of its meaning in different areas, including inverse problems, statistics, machine learning, and deep learning.