Dimensionality Reduction in Machine Learning
暫譯: 機器學習中的降維技術
Rad, Jamal Amani, Chakraverty, Snehashish, Parand, Kourosh
- 出版商: Morgan Kaufmann
- 出版日期: 2025-02-05
- 售價: $6,460
- 貴賓價: 9.5 折 $6,137
- 語言: 英文
- 頁數: 330
- 裝訂: Quality Paper - also called trade paper
- ISBN: 0443328188
- ISBN-13: 9780443328183
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相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding. Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.
商品描述(中文翻譯)
《機器學習中的降維》涵蓋了降維演算法的數學和程式設計兩個方面，並在各個方面進行比較。第一部分介紹了機器學習和數據生命週期，章節涵蓋了機器學習的基本概念、機器學習所需的基本數學以及特徵選擇的方法和概念。第二部分涵蓋了降維的線性方法，章節包括主成分分析（Principal Component Analysis）和線性判別分析（Linear Discriminant Analysis）。第三部分涵蓋了降維的非線性方法，章節包括線性局部嵌入（Linear Local Embedding）、多維縮放（Multi-dimensional Scaling）和t-分佈隨機鄰居嵌入（t-distributed Stochastic Neighbor Embedding）。
最後，第四部分涵蓋了降維的深度學習方法，章節包括特徵提取和深度學習（Feature Extraction and Deep Learning）、自編碼器（Autoencoders）以及通過群體作用進行的深度學習降維。透過這種逐步結構和應用的程式碼範例，讀者能夠將降維演算法應用於不同類型的數據，包括表格數據、文本數據和圖像數據。
