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標(biāo)題: Titlebook: Deep Learning Foundations; Taeho Jo Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature [打印本頁(yè)]

作者: 嚴(yán)峻 時(shí)間: 2025-3-21 19:12
書(shū)目名稱(chēng)Deep Learning Foundations影響因子(影響力)

書(shū)目名稱(chēng)Deep Learning Foundations影響因子(影響力)學(xué)科排名

書(shū)目名稱(chēng)Deep Learning Foundations網(wǎng)絡(luò)公開(kāi)度

書(shū)目名稱(chēng)Deep Learning Foundations網(wǎng)絡(luò)公開(kāi)度學(xué)科排名

書(shū)目名稱(chēng)Deep Learning Foundations被引頻次

書(shū)目名稱(chēng)Deep Learning Foundations被引頻次學(xué)科排名

書(shū)目名稱(chēng)Deep Learning Foundations年度引用

書(shū)目名稱(chēng)Deep Learning Foundations年度引用學(xué)科排名

書(shū)目名稱(chēng)Deep Learning Foundations讀者反饋

書(shū)目名稱(chēng)Deep Learning Foundations讀者反饋學(xué)科排名

作者: 水槽 時(shí)間: 2025-3-21 23:07
Supervised Learningameters are optimized for minimizing the error between the desired output and the computed one. In the supervised learning process, the training examples, each of which is labeled with its own target output, and the given learning algorithm are trained with them. Supervised learning algorithms are a

作者: 神圣在玷污 時(shí)間: 2025-3-22 01:17

作者: 去掉 時(shí)間: 2025-3-22 05:28
Ensemble Learninging the problems such as classification, regression, and clustering, more reliably by combining multiple machine learning algorithms with each other. The typical schemes of ensemble learning are the voting which is the process of deciding the final answer by considering ones of multiple machine lear

作者: Daily-Value 時(shí)間: 2025-3-22 10:55

作者: CAND 時(shí)間: 2025-3-22 16:06

作者: CAND 時(shí)間: 2025-3-22 18:19

作者: 吃掉 時(shí)間: 2025-3-23 00:13
Deep Linear Classifierfines its dual parallel hyperplanes with the maximal margin between them as the classification boundary. Even if the SVM is viewed as a deep learning algorithm, compared with the simple linear classifier, by itself, it may be modified into its further deep versions by attaching the input encoding an

作者: 賠償 時(shí)間: 2025-3-23 02:38
Multiple Layer Perceptrony Rosenblatt in the 1950s. In the architecture of MLP, there are three layers: the input layer, the hidden layer, and the output layer. A layer is connected to its next layer with the feedforward direction, and the weights are updated in its learning process in the backward direction. This chapter i

作者: 夸張 時(shí)間: 2025-3-23 08:18

作者: Obituary 時(shí)間: 2025-3-23 11:05

作者: AGGER 時(shí)間: 2025-3-23 16:08
Convolutional Neural Networkse convolutional neural networks in Chap. .. The pooling layers and the convolution layers are added as the feature extraction part to the MLP. There are two parts in the architecture of the convolutional neural networks: the feature extraction which is the alternative layers of the pooling and the l

作者: 功多汁水 時(shí)間: 2025-3-23 21:14
Index Expansionocess of adding more words which are relevant to ones in an input text. In the index expansion process, an input text is indexed into a list of words, their associated words are retrieved from external sources, and they are added to the list of words. There are three groups of words in indexing a te

作者: Acumen 時(shí)間: 2025-3-24 02:06
Text Summarizationrts as the summary. In the process of the text summarization, a text is partitioned into paragraphs, and important ones among them are selected as its summary. The text summarization is viewed as mapping a text into a hidden text in implementing the textual deep learning. This section is intended to

作者: Jubilation 時(shí)間: 2025-3-24 03:13

作者: 弄皺 時(shí)間: 2025-3-24 09:03

作者: 1分開(kāi) 時(shí)間: 2025-3-24 13:22
Design for Six Sigma+Lean Toolsethich partitions the training set into subsets and the vertical partition which partitions the attribute set. This chapter is intended to describe the ensemble learning as an advanced type of advanced learning.

作者: PRISE 時(shí)間: 2025-3-24 18:04
Ensemble Learninghich partitions the training set into subsets and the vertical partition which partitions the attribute set. This chapter is intended to describe the ensemble learning as an advanced type of advanced learning.

作者: choleretic 時(shí)間: 2025-3-24 21:07
Supervised Learningles, each of which is labeled with its own target output, and the given learning algorithm are trained with them. Supervised learning algorithms are applied to classification and regression. This chapter is intended to review the supervised learning as a kind of swallow learning, before studying the deep learning.

作者: labile 時(shí)間: 2025-3-24 23:58
Multiple Layer Perceptronnected to its next layer with the feedforward direction, and the weights are updated in its learning process in the backward direction. This chapter is intended to describe the MLP with respect to the architecture, the computation process, and the learning process.

作者: CLOWN 時(shí)間: 2025-3-25 05:27

作者: 條街道往前推 時(shí)間: 2025-3-25 10:18
Restricted Boltzmann Machineariables which are given for restoring the input. The stacked version of multiple RBMs which is called belief networks is a kind of deep neural networks. This chapter is intended to describe the RBM, together with the stacked version with respect to its learning process.

作者: enfeeble 時(shí)間: 2025-3-25 12:44
Text Summarization summary. The text summarization is viewed as mapping a text into a hidden text in implementing the textual deep learning. This section is intended to describe the text summarization with the view of implementing the textual deep learning.

作者: 決定性 時(shí)間: 2025-3-25 19:32
Textual Deep Operationsext called hidden text before encoding it into a numerical vector. The role of the textual deep operations is to preprocess a text in implementing the textual deep learning. This chapter is intended to describe the textual pooling and the textual convolution as the main textual deep operations.

作者: 等級(jí)的上升 時(shí)間: 2025-3-25 22:45

作者: Clinch 時(shí)間: 2025-3-26 02:18

作者: MAUVE 時(shí)間: 2025-3-26 04:46
C. Zecca,C. McGinley,R. Griffithse output decoding. As the advanced schemes, a decision tree is modified into its unsupervised version, and its original and modified versions are combined with each other into the stacked version. This chapter is intended to describe the schemes of modifying the decision tree into its deep versions.

作者: 溺愛(ài) 時(shí)間: 2025-3-26 09:25

作者: 嘲笑 時(shí)間: 2025-3-26 13:20

作者: 縮短 時(shí)間: 2025-3-26 16:51
Ajit Gopal,Amit Kundal,Ajay Mathranixt: the expansion group which contains very important words demanding their associated ones, the inclusion group which contains medium ones which are included as index, and the removal group which contains trivial ones which should be excluded from the index. This chapter is intended to describe the text indexing process and the index expansion.

作者: 才能 時(shí)間: 2025-3-26 21:17
arning algorithms for further analysis.Details how deep learThis book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervi

作者: 向宇宙 時(shí)間: 2025-3-27 03:41
Design for Six Sigma+Lean Toolsetles, each of which is labeled with its own target output, and the given learning algorithm are trained with them. Supervised learning algorithms are applied to classification and regression. This chapter is intended to review the supervised learning as a kind of swallow learning, before studying the deep learning.

作者: licence 時(shí)間: 2025-3-27 06:10

作者: MERIT 時(shí)間: 2025-3-27 11:05
https://doi.org/10.1007/978-3-030-69209-4connection from a neuron as an advanced model. In the feedback connection, a previous output value is used as an input. This chapter is intended to describe the recurrent neural networks and the variants with respect to the connection and the learning process.

作者: 毛細(xì)血管 時(shí)間: 2025-3-27 16:00
Manohar Mahato,Amarendra Kumar Dasariables which are given for restoring the input. The stacked version of multiple RBMs which is called belief networks is a kind of deep neural networks. This chapter is intended to describe the RBM, together with the stacked version with respect to its learning process.

作者: INTER 時(shí)間: 2025-3-27 19:41
Supradip Das,Amarendra Kumar Das summary. The text summarization is viewed as mapping a text into a hidden text in implementing the textual deep learning. This section is intended to describe the text summarization with the view of implementing the textual deep learning.

作者: 租約 時(shí)間: 2025-3-27 23:31

作者: 異端邪說(shuō)2 時(shí)間: 2025-3-28 03:29
Book 2023zmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

作者: CLEFT 時(shí)間: 2025-3-28 07:00
Book 2023ep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms

作者: Compassionate 時(shí)間: 2025-3-28 10:47
https://doi.org/10.1007/978-3-658-00828-4nced learning types than the deep learning: the kernel-based learning, the ensemble learning, and the semi-supervised learning. This chapter is intended to describe the deep learning conceptually for providing the introduction.

作者: Pelvic-Floor 時(shí)間: 2025-3-28 18:11

作者: Unsaturated-Fat 時(shí)間: 2025-3-28 19:39

作者: muscle-fibers 時(shí)間: 2025-3-29 01:53
Deep Probabilistic Learningvised versions are combined with each other into their stacked version. This chapter is intended to describe the schemes of modifying the two probabilistic learning algorithms into their deep versions.

作者: Prognosis 時(shí)間: 2025-3-29 03:06

作者: mighty 時(shí)間: 2025-3-29 07:29

作者: configuration 時(shí)間: 2025-3-29 14:21
Deep Decision Treee output decoding. As the advanced schemes, a decision tree is modified into its unsupervised version, and its original and modified versions are combined with each other into the stacked version. This chapter is intended to describe the schemes of modifying the decision tree into its deep versions.

作者: 水獺 時(shí)間: 2025-3-29 16:19