| 書目名稱 | Computational Methods for Deep Learning | | 副標(biāo)題 | Theory, Algorithms, | | 編輯 | Wei Qi Yan | | 視頻video | http://file.papertrans.cn/233/232711/232711.mp4 | | 概述 | Explores advanced topics in deep learning encompassing transformer models, control theory, and graph neural networks.Presents detailed mathematical descriptions and algorithms for generative pre-train | | 叢書名稱 | Texts in Computer Science | | 圖書封面 |  | | 描述 | .The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.?.The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI).?. .This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.. | | 出版日期 | Textbook 2023Latest edition | | 關(guān)鍵詞 | Deep Learning; Machine Learning; Pattern Analysis; Manifold Learning; Machine Vision; Reinforcement Learn | | 版次 | 2 | | doi | https://doi.org/10.1007/978-981-99-4823-9 | | isbn_ebook | 978-981-99-4823-9Series ISSN 1868-0941 Series E-ISSN 1868-095X | | issn_series | 1868-0941 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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