解剖深度學習原理
解剖深度學習原理
從0實現深度學習庫
About the Book
本書深入淺出地介紹了深度學習的基本原理和實現過程,用python的numpy庫從底層而不是藉助於現有的深度學習庫從零構建了自己的深度學習庫。在介紹基本的Python編程、微積分和概率統計知識基礎上,按照深度學習的發展脈絡依次介紹了回歸模型、神經網絡、卷積神經網絡、循環神經網絡、生成網絡等深度學習的核心基礎知識,在深入淺出地剖析原理的同時給出了詳細的代碼實現過程。如同不是教你如何使用武器、手機而是教你如何自己製造武器、手機,本書不是已有深度學習庫的使用教程而是剖析如何從0開發深度學習庫。這種從0開始的原理與代碼實現結合的方式可以使讀者更好地理解深度學習的基本原理和流行的深度學習庫的設計思想。
Table of Contents
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1.1.2 對象、print()函數、類型轉換、註釋、變量、input()函數
[下標運算符[]](#下標運算符)
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math數學包 - 全局變量和局部變量 - Anonymous/Lambda Function(匿名/lambda函數) - 嵌套函數、閉包 - yield和生成器
9. 添加、重複與鋪設、合併與分裂、邊緣填充、添加軸與交換軸 - 重複repeat() - 鋪設tile() - 合併concatenate() - 疊加stack() - column_stack()、hstack()、vstack() - 分裂split() - 邊緣填充 - 添加軸 - 交換軸
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