Neural Networks 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Neural Networks

简体网页||繁体网页
Raul Rojas 作者
Springer
译者
1996-1-1 出版日期
502 页数
USD 99.00 价格
Paperback
丛书系列
9783540605058 图书编码

Neural Networks 在线电子书 图书标签: 机器学习  Math  CS  2016  计算机  歐洲  機器學習  數學   


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发表于2024-11-26


Neural Networks 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Neural Networks 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Neural Networks 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Neural Networks 在线电子书 用户评价

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NN的研究虽然看似过时但显然比其他ML的model更deep更grounded。arithmetic circuit自己那么坑填不上就别怪MLer胡乱整unprovable的算法了,纵然嚷着有AG,大炮也不一定能打掉蚊子,有句话说好,It seems, however, that the current mathematical knowledge is very far from being able to prove even simpler claims.

评分

NN的研究虽然看似过时但显然比其他ML的model更deep更grounded。arithmetic circuit自己那么坑填不上就别怪MLer胡乱整unprovable的算法了,纵然嚷着有AG,大炮也不一定能打掉蚊子,有句话说好,It seems, however, that the current mathematical knowledge is very far from being able to prove even simpler claims.

评分

NN的研究虽然看似过时但显然比其他ML的model更deep更grounded。arithmetic circuit自己那么坑填不上就别怪MLer胡乱整unprovable的算法了,纵然嚷着有AG,大炮也不一定能打掉蚊子,有句话说好,It seems, however, that the current mathematical knowledge is very far from being able to prove even simpler claims.

评分

NN的研究虽然看似过时但显然比其他ML的model更deep更grounded。arithmetic circuit自己那么坑填不上就别怪MLer胡乱整unprovable的算法了,纵然嚷着有AG,大炮也不一定能打掉蚊子,有句话说好,It seems, however, that the current mathematical knowledge is very far from being able to prove even simpler claims.

评分

NN的研究虽然看似过时但显然比其他ML的model更deep更grounded。arithmetic circuit自己那么坑填不上就别怪MLer胡乱整unprovable的算法了,纵然嚷着有AG,大炮也不一定能打掉蚊子,有句话说好,It seems, however, that the current mathematical knowledge is very far from being able to prove even simpler claims.

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Neural Networks 在线电子书 图书描述

Neural networks are a computing paradigm that is finding increasing attention among computer scientists. In this book, theoretical laws and models previously scattered in the literature are brought together into a general theory of artificial neural nets. Always with a view to biology and starting with the simplest nets, it is shown how the properties of models change when more general computing elements and net topologies are introduced. Each chapter contains examples, numerous illustrations, and a bibliography. The book is aimed at readers who seek an overview of the field or who wish to deepen their knowledge. It is suitable as a basis for university courses in neurocomputing.

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