- Добавил: literator
- Дата: 25-11-2022, 03:51
- Комментариев: 0
![Statistical Foundations of Actuarial Learning and its Applications](/uploads/posts/2022-11/1669337557_2471_statistical_foundations_of_actuarial_l_arning_and_its_applications.jpg)
Автор: Mario V. Wuthrich, Michael Merz
Издательство: Springer
Год: 2023
Страниц: 611
Язык: английский
Формат: pdf (true)
Размер: 24.0 MB
Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern Machine Learning tools such as neural networks and text recognition to improve predictive modeling with complex features. In the sequel, we introduce Deep Learning models. In this chapter these Deep Learning models will be based on fully-connected feed-forward neural networks. We present these networks as an extension of Generalized Linear Models (GLMs). These networks perform feature engineering themselves. We discuss how networks achieve this, and we explain how networks are used for predictive modeling. There is a vastly growing literature on Deep Learning with networks, the classical reference is the book of Goodfellow et al., but also the numerous tutorials around the open-source Deep Learning libraries TensorFlow, Keras or PyTorch give an excellent overview of the state-of-the-art in this field.