Learning Deep Architectures for AI

作者:Yoshua Bengio

页数:136

价格:695.00 元

装帧:散装

isbn号码:9781601982940

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.

读后感

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...

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...

讲的比较清晰，提供了关键的数学计算内容，作为综述来看是很不错的选择。但是不亲自推一遍细节很难透彻理解，需要一些机器学习、随机过程、信息论和最优化理论的知识。理论框架介绍的很清楚，有助于理解目前各类变种。  

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...

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...

用户评价

RBM开始实在跟不上了，弃…前面的intuition挺有意思的…

弃了

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。

Deep Belief Networks / Restricted Boltzmann Machine

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。