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Explores funding science and technology for disaster risk reduction, emphasizing global differences, research focus, resource limitations, and future investments.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Introduces the role of engineers in facing risks and the evolution of disasters in engineering, emphasizing the need for a scientific approach to safety and reliability.
