Titlebook: Explainable AI with Python; Leonida Gianfagna,Antonio Di Cecco Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

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紅潤

colloquial

Shiriki Kumanyika,Ross C. Brownson, XAI can be achieved by looking at the internals with the proper interpretations of the weights and parameters that build the model. We will make practical examples (using Python code) that will deal with the quality of wine, the survival properties in a .-like disaster, and for the ML-addicted the

的染料

https://doi.org/10.1007/978-1-4614-4839-6 that . .. We also provided in Table . of Chap. . (don’t worry to look at it now, we will start again from this table in the following) a set of operational criteria based on question to distinguish between interpretability as a lighter form of explainability. As we saw, explainability is able to an

Melanocytes

Paul Tae-Woo Lee,Jasmine Siu Lee Lam as shown by Goodfellow et al. (2014), the first one has been classified as a panda by a NN with 55.7% confidence, while the second has been classified by the same NN as a gibbon with 99.3% confidence. What is happening here? The first thoughts are about some mistakes in designing or training the NN

北京人起源

Mohsen Golalikhani,Mark H. KarwanFor our purposes we place the birth of AI with the seminal work of Alan Turing (1950) in which the author posed the question “Can machines think?” and the later famous mental experiment proposed by Searle called the ..

北京人起源

Books, NIH and Journal Helpful ReferencesThis chapter is a bridge between the high-level overview of XAI presented in Chap. . and the hands-on work with XAI methods that we will start in Chap. .. The chapter will introduce a series of key concepts and a more complete terminology as you will find in literature and papers.

歌劇等

冷淡周邊

https://doi.org/10.1007/978-3-031-22727-1In this chapter, we will talk about XAI methods for Deep Learning models.

奴才

Hierarchical Data Format 5 : HDF5We reached the end of this journey. In this chapter, we close the loop presenting the full picture of our point of view on XAI; in particular, we will get back to our proposed flow for XAI commenting again on it but keeping in mind all the methods we discussed.