標(biāo)題: Titlebook: Geometry of Deep Learning; A Signal Processing Jong Chul Ye Textbook 2022 The Editor(s) (if applicable) and The Author(s), under exclusive [打印本頁] 作者: 淹沒 時(shí)間: 2025-3-21 18:57
書目名稱Geometry of Deep Learning影響因子(影響力)
書目名稱Geometry of Deep Learning影響因子(影響力)學(xué)科排名
書目名稱Geometry of Deep Learning網(wǎng)絡(luò)公開度
書目名稱Geometry of Deep Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Geometry of Deep Learning被引頻次
書目名稱Geometry of Deep Learning被引頻次學(xué)科排名
書目名稱Geometry of Deep Learning年度引用
書目名稱Geometry of Deep Learning年度引用學(xué)科排名
書目名稱Geometry of Deep Learning讀者反饋
書目名稱Geometry of Deep Learning讀者反饋學(xué)科排名
作者: 反省 時(shí)間: 2025-3-21 20:40 作者: 尖酸一點(diǎn) 時(shí)間: 2025-3-22 00:59 作者: Cuisine 時(shí)間: 2025-3-22 07:48 作者: Congregate 時(shí)間: 2025-3-22 11:03 作者: cardiac-arrest 時(shí)間: 2025-3-22 15:53
Einführung in die Volkswirtschaftslehreks, brain networks, molecule networks, etc. See some examples in Fig. 8.1. In fact, the complex interaction in real systems can be described by different forms of graphs, so that graphs can be a ubiquitous tool for representing complex systems.作者: cardiac-arrest 時(shí)間: 2025-3-22 19:46 作者: 離開可分裂 時(shí)間: 2025-3-22 23:00 作者: Constituent 時(shí)間: 2025-3-23 05:10 作者: 連鎖 時(shí)間: 2025-3-23 08:12 作者: Hangar 時(shí)間: 2025-3-23 13:34 作者: burnish 時(shí)間: 2025-3-23 15:00
Graph Neural Networksks, brain networks, molecule networks, etc. See some examples in Fig. 8.1. In fact, the complex interaction in real systems can be described by different forms of graphs, so that graphs can be a ubiquitous tool for representing complex systems.作者: 急性 時(shí)間: 2025-3-23 19:50 作者: narcotic 時(shí)間: 2025-3-24 01:28
978-981-16-6048-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: 彎彎曲曲 時(shí)間: 2025-3-24 03:26
Geometry of Deep Learning978-981-16-6046-7Series ISSN 1612-3956 Series E-ISSN 2198-3283 作者: CROAK 時(shí)間: 2025-3-24 09:42
Die Viertelgrenzen und die Halbgrenze,In this chapter, we briefly review the basic mathematical concepts that are required to understand the materials of this book.作者: 杠桿支點(diǎn) 時(shí)間: 2025-3-24 13:06 作者: 肉身 時(shí)間: 2025-3-24 16:30 作者: optic-nerve 時(shí)間: 2025-3-24 23:02 作者: 外觀 時(shí)間: 2025-3-25 03:03
https://doi.org/10.1007/978-3-322-96693-3The last part of our voyage toward the understanding of the geometry of deep learning concerns perhaps the most exciting aspect of deep learning—作者: 煩擾 時(shí)間: 2025-3-25 04:23
Mathematical PreliminariesIn this chapter, we briefly review the basic mathematical concepts that are required to understand the materials of this book.作者: 獨(dú)裁政府 時(shí)間: 2025-3-25 09:44 作者: Anthem 時(shí)間: 2025-3-25 13:00
Reproducing Kernel Hilbert Space, Representer TheoremOne of the key concepts in machine learning is the feature space, which is often referred to as the .. A feature space is usually a higher or lower-dimensional space than the original one where the input data lie (which is often referred to as the .).作者: 就職 時(shí)間: 2025-3-25 18:36
Normalization and AttentionIn this chapter, we will discuss very exciting and rapidly evolving technical fields of deep learning: . and ..作者: septicemia 時(shí)間: 2025-3-25 21:07 作者: Flustered 時(shí)間: 2025-3-26 02:55 作者: sultry 時(shí)間: 2025-3-26 04:52
https://doi.org/10.1007/978-3-322-84383-8his method is mainly used to predict and find the cause-and-effect relationship between variables. For example, in a linear regression, a researcher tries to find the line that best fits the data according to a certain mathematical criterion (see Fig. 3.1a).作者: 小口啜飲 時(shí)間: 2025-3-26 09:53
https://doi.org/10.1007/978-3-642-91616-8f neurons and connections in a network may be significantly high. One of the amazing aspects of biological neural networks is that when the neurons are connected to each other, higher-level intelligence, which cannot be observed from a single neuron, emerges.作者: capillaries 時(shí)間: 2025-3-26 13:41 作者: Fallibility 時(shí)間: 2025-3-26 18:56 作者: 泥土謙卑 時(shí)間: 2025-3-26 22:56
Einführung in die Volkswirtschaftslehreks, brain networks, molecule networks, etc. See some examples in Fig. 8.1. In fact, the complex interaction in real systems can be described by different forms of graphs, so that graphs can be a ubiquitous tool for representing complex systems.作者: Outshine 時(shí)間: 2025-3-27 03:58
Paul Engelkamp,Friedrich L. Sell neural network learn? How does a deep neural network, especially a CNN, accomplish these goals? The full answer to these basic questions is still a long way off. Here are some of the insights we’ve obtained while traveling towards that destination. In particular, we explain why the classic approach作者: Enliven 時(shí)間: 2025-3-27 06:54
ally gradient-based local update schemes. However, the biggest obstacle recognized by the entire community is that the loss surfaces of deep neural networks are extremely non-convex and not even smooth. This non-convexity and non-smoothness make the optimization unaffordable to analyze, and the main作者: infringe 時(shí)間: 2025-3-27 11:19
https://doi.org/10.1007/978-3-642-50938-4ective of classic machine learning. In particular, the number of trainable parameters in deep neural networks is often greater than the training data set, this situation being notorious for overfitting from the point of view of classical statistical learning theory. However, empirical results have s作者: sundowning 時(shí)間: 2025-3-27 16:40
Verteilungen auf der reellen Achse,revolution”. Despite the great successes of deep learning in various areas, there is a tremendous lack of rigorous mathematical foundations which enable us to understand why deep learning methods perform well.作者: Crohns-disease 時(shí)間: 2025-3-27 20:19 作者: Frenetic 時(shí)間: 2025-3-27 22:26
Biological Neural Networksf neurons and connections in a network may be significantly high. One of the amazing aspects of biological neural networks is that when the neurons are connected to each other, higher-level intelligence, which cannot be observed from a single neuron, emerges.作者: Instrumental 時(shí)間: 2025-3-28 04:34
Artificial Neural Networks and Backpropagation have been made to model all aspects of the biological neuron using a mathematical model, all of them may not be necessary: rather, there are some key aspects that should not be neglected when modeling a neuron. This includes the weight adaptation and the nonlinearity.作者: 手工藝品 時(shí)間: 2025-3-28 07:43
Convolutional Neural Networksptrons, which we discussed in the previous chapter, usually require fully connected networks, where each neuron in one layer is connected to all neurons in the next layer. Unfortunately, this type of connections inescapably increases the number of weights.作者: OTTER 時(shí)間: 2025-3-28 11:49
Graph Neural Networksks, brain networks, molecule networks, etc. See some examples in Fig. 8.1. In fact, the complex interaction in real systems can be described by different forms of graphs, so that graphs can be a ubiquitous tool for representing complex systems.作者: 悲痛 時(shí)間: 2025-3-28 15:38
Geometry of Deep Neural Networks neural network learn? How does a deep neural network, especially a CNN, accomplish these goals? The full answer to these basic questions is still a long way off. Here are some of the insights we’ve obtained while traveling towards that destination. In particular, we explain why the classic approach作者: CLEAR 時(shí)間: 2025-3-28 20:00
Deep Learning Optimizationally gradient-based local update schemes. However, the biggest obstacle recognized by the entire community is that the loss surfaces of deep neural networks are extremely non-convex and not even smooth. This non-convexity and non-smoothness make the optimization unaffordable to analyze, and the main作者: 異常 時(shí)間: 2025-3-28 22:54 作者: 生命 時(shí)間: 2025-3-29 03:53
Summary and Outlookrevolution”. Despite the great successes of deep learning in various areas, there is a tremendous lack of rigorous mathematical foundations which enable us to understand why deep learning methods perform well.作者: ARENA 時(shí)間: 2025-3-29 08:27 作者: 規(guī)范要多 時(shí)間: 2025-3-29 13:42 作者: Melatonin 時(shí)間: 2025-3-29 17:59
tworks are extremely non-convex and not even smooth. This non-convexity and non-smoothness make the optimization unaffordable to analyze, and the main concern was whether popular gradient-based approaches might fall into local minimizers.作者: CLEAR 時(shí)間: 2025-3-29 23:09 作者: malign 時(shí)間: 2025-3-30 00:04 作者: conscribe 時(shí)間: 2025-3-30 05:14 作者: 虛弱的神經(jīng) 時(shí)間: 2025-3-30 09:41
Deep Learning Optimizationtworks are extremely non-convex and not even smooth. This non-convexity and non-smoothness make the optimization unaffordable to analyze, and the main concern was whether popular gradient-based approaches might fall into local minimizers.作者: resuscitation 時(shí)間: 2025-3-30 15:08
Generalization Capability of Deep Learningset, this situation being notorious for overfitting from the point of view of classical statistical learning theory. However, empirical results have shown that a deep neural network generalizes well at the test phase, resulting in high performance for the unseen data.作者: Vulnerable 時(shí)間: 2025-3-30 19:44
1612-3956 to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are desc978-981-16-6048-1978-981-16-6046-7Series ISSN 1612-3956 Series E-ISSN 2198-3283 作者: Maximizer 時(shí)間: 2025-3-30 23:47
