Introduction As a branch of mathematics, topology describes the properties and structures of space in a unique way. In recent years, geometry and topology have been widely applied in machine learning, especially topological models, which play a significant role in data representation and feature extraction. Topological Data Analysis (TDA) is rooted in algebraic topology and … Read more
So far, a major concern about neural networks is that they are difficult to interpret black boxes. This article primarily explains theoretically why neural networks perform so well in pattern recognition and classification. Essentially, they distort and transform the original input through layers of affine transformations and nonlinear transformations until different categories can be easily … Read more
Source | OSCHINA Community Author | OneFlow Deep Learning Framework Original link: https://my.oschina.net/oneflow/blog/5559651 So far, one major concern about neural networks is that they are difficult to interpret black boxes. This article primarily aims to theoretically understand why neural networks perform so well in pattern recognition and classification, fundamentally distorting and transforming the original input … Read more
To date, a major concern regarding neural networks is that they are difficult to interpret black boxes. This article primarily aims to understand theoretically why neural networks perform so well in pattern recognition and classification. The essence lies in the fact that they distort and transform the original input through layers of affine transformations and … Read more