標(biāo)題: Titlebook: Computational Methods for Deep Learning; Theory, Algorithms, Wei Qi Yan Textbook 2023Latest edition The Editor(s) (if applicable) and The [打印本頁(yè)] 作者: Braggart 時(shí)間: 2025-3-21 20:00
書(shū)目名稱Computational Methods for Deep Learning影響因子(影響力)
書(shū)目名稱Computational Methods for Deep Learning影響因子(影響力)學(xué)科排名
書(shū)目名稱Computational Methods for Deep Learning網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Computational Methods for Deep Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Computational Methods for Deep Learning被引頻次
書(shū)目名稱Computational Methods for Deep Learning被引頻次學(xué)科排名
書(shū)目名稱Computational Methods for Deep Learning年度引用
書(shū)目名稱Computational Methods for Deep Learning年度引用學(xué)科排名
書(shū)目名稱Computational Methods for Deep Learning讀者反饋
書(shū)目名稱Computational Methods for Deep Learning讀者反饋學(xué)科排名
作者: itinerary 時(shí)間: 2025-3-21 21:46
,Convolutional Neural Networks and?Recurrent Neural Networks,ally Region-based CNN (R-CNN), Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO). Capsule Neural Network (CapsNet)?has taken a topological structure?of a scene into consideration. The output will be a vector to reflect this geometric relationship.作者: 發(fā)酵 時(shí)間: 2025-3-22 04:11 作者: NIB 時(shí)間: 2025-3-22 07:31 作者: SPURN 時(shí)間: 2025-3-22 12:06 作者: infelicitous 時(shí)間: 2025-3-22 16:39 作者: infelicitous 時(shí)間: 2025-3-22 20:11 作者: HUSH 時(shí)間: 2025-3-23 01:07 作者: intention 時(shí)間: 2025-3-23 04:40 作者: 情感脆弱 時(shí)間: 2025-3-23 07:57 作者: CAMP 時(shí)間: 2025-3-23 10:48
,Convolutional Neural Networks and?Recurrent Neural Networks,ally Region-based CNN (R-CNN), Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO). Capsule Neural Network (CapsNet)?has taken a topological structure?of a scene into consideration. The output will be a vector to reflect this geometric relationship.作者: Serenity 時(shí)間: 2025-3-23 14:20 作者: Agility 時(shí)間: 2025-3-23 18:57
Manifold Learning and Graph Neural Network,t of basestone. We need to introduce our readers why we should study graphs, what we can benefit from the graphs. Furthermore, we will introduce graph neural networks (GNN) and how to combine GNN with manifold learning together.作者: tooth-decay 時(shí)間: 2025-3-24 00:43
,Transfer Learning and?Ensemble Learning,d hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust classifier for pattern classification.作者: 等待 時(shí)間: 2025-3-24 02:43
Computational Methods for Deep Learning978-981-99-4823-9Series ISSN 1868-0941 Series E-ISSN 1868-095X 作者: 漂白 時(shí)間: 2025-3-24 07:21 作者: NOTCH 時(shí)間: 2025-3-24 13:46 作者: 摘要 時(shí)間: 2025-3-24 15:52
Sub-Saharan Africa’s Development Challengest of basestone. We need to introduce our readers why we should study graphs, what we can benefit from the graphs. Furthermore, we will introduce graph neural networks (GNN) and how to combine GNN with manifold learning together.作者: 形狀 時(shí)間: 2025-3-24 21:13
https://doi.org/10.1057/9780230618435d hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust classifier for pattern classification.作者: TOM 時(shí)間: 2025-3-25 02:56 作者: 出血 時(shí)間: 2025-3-25 05:32 作者: Magisterial 時(shí)間: 2025-3-25 09:58
Trade Unions and Class ConsciousnessThis chapter covers the fundamentals of deep learning, therefore, we present relevant knowledge in chronological order so as to fully introduce the history of deep learning development; meanwhile, we review how to use MATLAB, TensorFlow, software R, and WEKA from New Zealand, etc., as typical platforms for developing deep learning applications.作者: 大笑 時(shí)間: 2025-3-25 14:19 作者: 混沌 時(shí)間: 2025-3-25 18:28
Introduction,This chapter covers the fundamentals of deep learning, therefore, we present relevant knowledge in chronological order so as to fully introduce the history of deep learning development; meanwhile, we review how to use MATLAB, TensorFlow, software R, and WEKA from New Zealand, etc., as typical platforms for developing deep learning applications.作者: Allege 時(shí)間: 2025-3-25 22:44
Reinforcement Learning,In this chapter, we introduce fundamental concepts of reinforcement learning?[.] such as Bellman equation, Q-learning, deep Q-learning, and double Q-learning. We detail why reinforcement learning?is thought as a method of deep learning.作者: Spinous-Process 時(shí)間: 2025-3-26 01:38 作者: heartburn 時(shí)間: 2025-3-26 07:52 作者: conceal 時(shí)間: 2025-3-26 10:18 作者: effrontery 時(shí)間: 2025-3-26 13:04 作者: Between 時(shí)間: 2025-3-26 18:58 作者: Frequency 時(shí)間: 2025-3-26 22:30
Textbook 2023Latest edition (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..作者: 生銹 時(shí)間: 2025-3-27 01:55
Textbook 2023Latest edition 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 in作者: 責(zé)難 時(shí)間: 2025-3-27 09:09 作者: 刺耳 時(shí)間: 2025-3-27 12:34
Book 2021arien und Gesetze für die Finanzbranche heraus. Der Beruf der Finanz- , Verm?gens- und FinanzierungsberaterInnen ist eine T?tigkeit, die st?ndige Weiterbildung erforderlich macht. Auch die Digitalisierung hat die Branche voll im Griff und Videoberatungen haben massiv an Bedeutung gewonnen. Doch die 作者: Innocence 時(shí)間: 2025-3-27 14:41 作者: circumvent 時(shí)間: 2025-3-27 20:22
