The principles of deep learning theory : an effective theory approach to understanding neural networks

フォーマット:

図書

責任表示:

Daniel A. Roberts, Sho Yaida ; based on research in collaboration with Boris Hanin

言語:

英語

出版情報:

New York : Cambridge University Press, 2022

形態:

x, 460 p. ; 27 cm

著者名:

書誌ID:

BC15660690

ISBN:

9781316519332 [1316519333] (: hbk)

子書誌情報

所蔵情報

タイトルが類似している資料

詳細

主題:	Deep learning (Machine learning)
分類・件名:	LCC : Q325.73 DC23 : 006.3/1 LCSH : Deep learning (Machine learning)
LCCN:	2021060635
注記:	Includes bibliographical references (p. 439-443) and index
TTLL:	eng

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1 図書 Dive into deep learning Zhang, Aston, Lipton, Zachary C., Li, Mu (Computer scientist), Smola, Alexander J. Cambridge University Press	7 図書 Artificial neural networks for image understanding Kulkarni, Arun D., 1947- Van Nostrand Reinhold
2 図書 Neural network principles Harvey, Robert L. Prentice Hall	8 図書 Learning with recurrent neural networks Hammer, Barbara Springer
3 図書 Understanding neural networks : a primer Cruz, Claude A. Graeme Pub. Co.	9 図書 Subspace learning of neural networks Lv, Jian Cheng, Yi, Zhang, Zhou, Jiliu CRC Press
4 図書 Artificial neural networks : approximation and learning theory White, Halbert Blackwell	10 図書 Neural networks and statistical learning Du, Ke-Lin, Swamy, M.N.S. Springer-Verlag
5 図書 Principles of high-performance processor design : for high performance computing, deep neural networks and data science Makino, Junichiro Springer	11 図書 On-line learning in neural networks Saad, David Cambridge University press
6 図書 The neurobiology of neural networks Gardner, Daniel, 1945- MIT Press	12 図書 Bayesian learning for neural networks Neal, Radford M. Springer-Verlag