Linear Algebra and Learning from Data (Hardcover)

Gilbert Strang

  • 出版商: Cambridge
  • 出版日期: 2019-02-28
  • 定價: $1,880
  • 售價: 9.5$1,786
  • 語言: 英文
  • 頁數: 432
  • 裝訂: Hardcover
  • ISBN: 0692196382
  • ISBN-13: 9780692196380
  • 相關分類: 線性代數 Linear-algebra
  • 立即出貨 (庫存 < 3)

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商品描述

This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text.

商品描述(中文翻譯)

這是一本教讀者了解深度學習步驟的教科書。線性代數是首要的,特別是奇異值、最小二乘法和矩陣分解。通常的目標是對大型數據矩陣進行低秩近似 A = CR (列-行),以查看其最重要的部分。這使用了應用線性代數的全部範疇,包括用於非常大的矩陣的隨機化。然後,深度學習創建了一個權重的大規模優化問題,通過梯度下降或更好的隨機梯度下降來解決。最後,本書介紹了完全連接的神經網絡和卷積神經網絡 (CNN) 的架構,以在數據中尋找模式。受眾:本書適合任何想學習數據如何通過矩陣方法進行減少和解釋的人。基於Strang教授所教授的第二門線性代數課程,他在訓練數據方面的講座廣為人知,本書從頭開始(四個基本子空間),並且完全無需第一本教材即可理解。