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Learning Deep Architectures for AI 2025 pdf epub mobi 電子書 下載

Learning Deep Architectures for AI
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Learning Deep Architectures for AI pdf epub mobi 著者簡介

Learning Deep Architectures for AI pdf epub mobi 圖書描述

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.

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發表於2025-02-02

Learning Deep Architectures for AI 2025 pdf epub mobi 電子書 下載

Learning Deep Architectures for AI 2025 pdf epub mobi 電子書 下載

Learning Deep Architectures for AI 2025 pdf epub mobi 電子書 下載


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Learning Deep Architectures for AI pdf epub mobi 讀後感

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具體內容： 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. 這句意思是k-1層架構可以錶示的函數需要的計算元素...

評分☆☆☆☆☆

具體內容： 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. 這句意思是k-1層架構可以錶示的函數需要的計算元素...

評分☆☆☆☆☆

具體內容： 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. 這句意思是k-1層架構可以錶示的函數需要的計算元素...

評分☆☆☆☆☆

講的比較清晰，提供瞭關鍵的數學計算內容，作為綜述來看是很不錯的選擇。但是不親自推一遍細節很難透徹理解，需要一些機器學習、隨機過程、信息論和最優化理論的知識。理論框架介紹的很清楚，有助於理解目前各類變種。  

評分☆☆☆☆☆

具體內容： 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. 這句意思是k-1層架構可以錶示的函數需要的計算元素...

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出版者:
作者:Yoshua Bengio
出品人:
頁數:136
譯者:
出版時間:
價格:695.00 元
裝幀:散裝
isbn號碼:9781601982940
叢書系列:

圖書標籤: 深度學習  機器學習  人工智能  神經網絡  AI  計算機  計算機科學  programming   

Learning Deep Architectures for AI 2025 pdf epub mobi 電子書 下載
想要找書就要到 本本書屋
立刻按 ctrl+D收藏本頁
你會得到大驚喜!!

Learning Deep Architectures for AI pdf epub mobi 用戶評價

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看得懵懵懂懂

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棄瞭

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Deep Belief Networks / Restricted Boltzmann Machine

評分☆☆☆☆☆

非常insightful的小書，把deep learning背後的philosophy以及現有的一些results闡述得很清楚。DL現在應用很廣，炒得很火，體係韆瘡百孔，這是好事。Bengio通過此書指瞭些明路。另此書不適閤DL入門。

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Learning Deep Architectures for AI 2025 pdf epub mobi 電子書 下載

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