標(biāo)題: Titlebook: Deep Learning: Algorithms and Applications; Witold Pedrycz,Shyi-Ming Chen Book 2020 Springer Nature Switzerland AG 2020 Computational Inte [打印本頁] 作者: 掩飾 時(shí)間: 2025-3-21 17:43
書目名稱Deep Learning: Algorithms and Applications影響因子(影響力)
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書目名稱Deep Learning: Algorithms and Applications網(wǎng)絡(luò)公開度
書目名稱Deep Learning: Algorithms and Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning: Algorithms and Applications被引頻次
書目名稱Deep Learning: Algorithms and Applications被引頻次學(xué)科排名
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書目名稱Deep Learning: Algorithms and Applications讀者反饋
書目名稱Deep Learning: Algorithms and Applications讀者反饋學(xué)科排名
作者: antiquated 時(shí)間: 2025-3-21 20:53
https://doi.org/10.1007/978-3-030-31760-7Computational Intelligence; Machine Learning; Computer Vision; Natural Language Processing; Deep Learnin作者: 有限 時(shí)間: 2025-3-22 00:31
978-3-030-31762-1Springer Nature Switzerland AG 2020作者: 踉蹌 時(shí)間: 2025-3-22 07:48
Deutsche Klassiker im Nationalsozialismuswork learn will only be able to learn a linear relation between input and the desired output. The chapter introduces the reader to why activation functions are useful and their immense importance in making deep learning successful. A detailed survey of several existing activation functions?is provid作者: 啞巴 時(shí)間: 2025-3-22 08:49 作者: 感染 時(shí)間: 2025-3-22 16:39
Abstand, nicht Widerstand: Max Kommerelltribute up?to . to the energy mix. However, the challenges that arise with the integration of those variable energy resources are various. Some of these tasks are short-term and long-term power generation forecasts, load?forecasts, integration of multiple numerical weather prediction (NWP) models, s作者: 感染 時(shí)間: 2025-3-22 19:00 作者: Tartar 時(shí)間: 2025-3-22 23:35 作者: 表狀態(tài) 時(shí)間: 2025-3-23 03:27
Literatur in der Weimarer Republik, making this possible because it can leverage the huge amount of training data that comes from autonomous car sensors. Automatic recognition of traffic light and vehicle signal is a perception module critical to autonomous vehicles because a deadly car accident could happen if a vehicle fails to fol作者: 馬籠頭 時(shí)間: 2025-3-23 05:49
https://doi.org/10.1007/978-3-476-04953-7as are utilised to access underwater environments. Up till now image data has been processed by human observers which is costly and often represents repetitive mundane work. Deep learning techniques that can automatically classify objects can increase the speed and the amounts of data that can be pr作者: 泥瓦匠 時(shí)間: 2025-3-23 11:20
https://doi.org/10.1007/978-3-476-00813-8ons. However, it is improbable that a single deterrent method would work for all bird species in a given area. An automatic bird identification system is needed in order to develop bird species level deterrent methods. This system is the first and necessary part of the entirety that is eventually ab作者: 無情 時(shí)間: 2025-3-23 17:55
https://doi.org/10.1007/978-3-476-00813-8lance systems security mechanism is to re-identify a person captured in one of the camera across different surveillance cameras. Re-identification has a major role in several applications like automated surveillance of universities, offices, malls, home and restricted environments like embassies or 作者: FOR 時(shí)間: 2025-3-23 19:48
Die Deutsche Literatur des Exils,ers a unique motion pattern of an individual. Gait refers to the study of locomotion in both humans and animals. It involves?the coordination of several parts of the human body: the brain, the spinal cord, the nerves, muscles, bones, and also joints. Gait analysis?has been studied for a variety of a作者: Neuropeptides 時(shí)間: 2025-3-24 01:49 作者: 輕浮思想 時(shí)間: 2025-3-24 03:37
Witold Pedrycz,Shyi-Ming ChenProvides a comprehensive and up-to-date overview of deep learning by discussing a range of methodological and algorithmic issues.Addresses implementations and case studies, identifying the best design作者: gratify 時(shí)間: 2025-3-24 08:41 作者: 萬神殿 時(shí)間: 2025-3-24 13:20
1860-949X mplementations and case studies, identifying the best designThis book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical pro作者: 繁榮地區(qū) 時(shí)間: 2025-3-24 16:24
Book 2020h the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradig作者: IRATE 時(shí)間: 2025-3-24 22:12 作者: 匯總 時(shí)間: 2025-3-25 00:54 作者: ornithology 時(shí)間: 2025-3-25 07:23 作者: 我的巨大 時(shí)間: 2025-3-25 10:24 作者: Root494 時(shí)間: 2025-3-25 14:29 作者: Highbrow 時(shí)間: 2025-3-25 16:04 作者: 合法 時(shí)間: 2025-3-25 22:21 作者: 祖?zhèn)髫?cái)產(chǎn) 時(shí)間: 2025-3-26 01:37 作者: 無節(jié)奏 時(shí)間: 2025-3-26 04:33
Deep Learning Application: Load Forecasting in Big Data of Smart Grids,es remain for accurate load forecasting using big data or large-scale datasets. This chapter addresses the problem of how to improve the forecasting results of loads in smart grids, using deep learning methods that have shown significant progress in various disciplines in recent years. The deep lear作者: excrete 時(shí)間: 2025-3-26 09:13 作者: intolerance 時(shí)間: 2025-3-26 15:15
Traffic Light and Vehicle Signal Recognition with High Dynamic Range Imaging and Deep Learning, making this possible because it can leverage the huge amount of training data that comes from autonomous car sensors. Automatic recognition of traffic light and vehicle signal is a perception module critical to autonomous vehicles because a deadly car accident could happen if a vehicle fails to fol作者: Abutment 時(shí)間: 2025-3-26 18:14
The Application of Deep Learning in Marine Sciences,as are utilised to access underwater environments. Up till now image data has been processed by human observers which is costly and often represents repetitive mundane work. Deep learning techniques that can automatically classify objects can increase the speed and the amounts of data that can be pr作者: 項(xiàng)目 時(shí)間: 2025-3-27 00:34
Deep Learning Case Study on Imbalanced Training Data for Automatic Bird Identification,ons. However, it is improbable that a single deterrent method would work for all bird species in a given area. An automatic bird identification system is needed in order to develop bird species level deterrent methods. This system is the first and necessary part of the entirety that is eventually ab作者: 濃縮 時(shí)間: 2025-3-27 04:20 作者: 無力更進(jìn) 時(shí)間: 2025-3-27 09:19 作者: 迅速飛過 時(shí)間: 2025-3-27 10:38
Deep Learning for Building Occupancy Estimation Using Environmental Sensors,ncy, which directly affects energy-related control systems in buildings. Among varieties of sensors for occupancy estimation, environmental sensors?have unique properties of non-intrusion and low-cost. In general, occupancy estimation using environmental sensors?contains feature engineering and lear作者: 繁榮中國 時(shí)間: 2025-3-27 16:40 作者: kyphoplasty 時(shí)間: 2025-3-27 21:31
Abstand, nicht Widerstand: Max Kommerellresent data in such a way that it is suited for the task at hand. Once the neural network has learned such a representation of the data in a supervised or semi-supervised manner, it makes it possible to utilize this representation in the various available tasks for renewable?energy. In our chapter, 作者: 他一致 時(shí)間: 2025-3-27 23:55 作者: 半身雕像 時(shí)間: 2025-3-28 02:25
Die Deutsche Literatur des Exils,structed within seconds, namely, orders of magnitude faster than existing solutions. Reconstructed models are free of human biases since no initial model or numerical technique tuning is required. This chapter is a significant extension of previous published material and provides a detailed explanat作者: 向外才掩飾 時(shí)間: 2025-3-28 10:08 作者: Decongestant 時(shí)間: 2025-3-28 14:21 作者: decipher 時(shí)間: 2025-3-28 16:31
https://doi.org/10.1007/978-3-476-00813-8captured images is naturally imbalanced. We applied distribution of the training data set to estimate the actual distribution of the bird species in the test area. Species identification is based on the image classifier that is a hybrid of hierarchical and cascade models. The main idea is to train c作者: essential-fats 時(shí)間: 2025-3-28 21:41 作者: 種類 時(shí)間: 2025-3-29 02:47 作者: enterprise 時(shí)間: 2025-3-29 05:26 作者: 胎兒 時(shí)間: 2025-3-29 09:29
Adversarial Examples in Deep Neural Networks: An Overview,behind the existence of adversarial examples as well as theories that consider the relation between the generalization error and adversarial robustness. Finally, various defenses against adversarial examples?are also discussed.作者: Assignment 時(shí)間: 2025-3-29 14:33
Representation Learning in Power Time Series Forecasting,resent data in such a way that it is suited for the task at hand. Once the neural network has learned such a representation of the data in a supervised or semi-supervised manner, it makes it possible to utilize this representation in the various available tasks for renewable?energy. In our chapter,
