发表于2024-05-15
Learning Deep Architectures for AI 2024 pdf epub mobi 电子书
具体内容: More precisely ,functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k-1 architecture. 这句意思是k-1层架构可以表示的函数需要的计算元素...
评分具体内容: More precisely ,functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k-1 architecture. 这句意思是k-1层架构可以表示的函数需要的计算元素...
评分具体内容: More precisely ,functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k-1 architecture. 这句意思是k-1层架构可以表示的函数需要的计算元素...
评分具体内容: More precisely ,functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k-1 architecture. 这句意思是k-1层架构可以表示的函数需要的计算元素...
评分具体内容: More precisely ,functions that can be compactly represented by a depth k architecture might require an exponential number of computational elements to be represented by a depth k-1 architecture. 这句意思是k-1层架构可以表示的函数需要的计算元素...
图书标签: 深度学习 机器学习 人工智能 神经网络 AI 计算机 计算机科学 programming
Theoretical results suggest that in order to learn the kind of complicated
functions that can represent high-level abstractions (e.g., in
vision, language, and other AI-level tasks), one may need deep architectures.
Deep architectures are composed of multiple levels of non-linear
operations, such as in neural nets with many hidden layers or in complicated
propositional formulae re-using many sub-formulae. Searching
the parameter space of deep architectures is a difficult task, but learning
algorithms such as those for Deep Belief Networks have recently been
proposed to tackle this problem with notable success, beating the stateof-
the-art in certain areas. This monograph discusses the motivations
and principles regarding learning algorithms for deep architectures, in
particular those exploiting as building blocks unsupervised learning of
single-layer models such as Restricted Boltzmann Machines, used to
construct deeper models such as Deep Belief Networks.
非常insightful的小书,把deep learning背后的philosophy以及现有的一些results阐述得很清楚。DL现在应用很广,炒得很火,体系千疮百孔,这是好事。Bengio通过此书指了些明路。另此书不适合DL入门。
评分相见恨晚。把Hinton的papers翻了个遍,没想到在这本书上才让我对RBM的认识最深刻。
评分Deep Belief Networks / Restricted Boltzmann Machine
评分看得懵懵懂懂
评分Deep Belief Networks / Restricted Boltzmann Machine
Learning Deep Architectures for AI 2024 pdf epub mobi 电子书