に同じ書籍を注文されています。
再度ご注文されますか?




Introduction to Machine Learning 4th ed. H 712 p. 20

Introduction to Machine Learning 4th ed. H 712 p. 20

著者:Alpaydin, Ethem


【重要事項説明】

1.手配先によって価格が異なります。
2.納期遅延や入手不能となる場合がございます。
3.海外のクリスマス休暇等、お正月等の長期休暇時期の発注は、納期遅延となる場合がございます。
4.天候(国内・海外)により空港の発着・貨物受入不能の発生により納期遅延となる場合がございます。
5.複数冊数のご注文の場合、分納となる場合がございます。
6.美品のご指定は承りかねます。

手配先:国内提携倉庫
  • 本体価格:¥14,511(税抜)
  • お届けまでの予想日数: 1週間
  • 在庫数:在庫あり
  • ※1冊以外はご注文いただけません。
  • 組合員価格:¥15,962 (税込)
  • ※ご注文のタイミングで引き当たらない場合がございます。
手配先:海外仕入USA
  • 現地価格:$85
  • お届けまでの予想日数: 3週間~4週間
  • 在庫数:
  • 組合員価格:¥15,495 (税込)
手配先:海外仕入UK他
  • 現地価格:£80
  • お届けまでの予想日数: 2週間~3週間
  • 在庫数:1
  • 組合員価格:¥18,806 (税込)
手配先:海外仕入UK他
  • 現地価格:€100.5
  • お届けまでの予想日数: 3週間~4週間
  • 在庫数:1
  • 組合員価格:¥19,971 (税込)

内容の説明

A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.

登録情報

商品コード:1031013228
出版社: The MIT Press
出版年月: 2020/03
ISBN-10: 0262043793
ISBN-13: 978-0-262-04379-3
出版国: アメリカ合衆国
装丁: hardcover/Geb./rel.
媒体: 冊子
ページ数: 712 p., 199 B&W ILLUS.
ジャンル: 人工知能



PAGE TOP