Titlebook: Handbook of Deep Learning Applications; Valentina Emilia Balas,Sanjiban Sekhar Roy,Pijush Book 2019 Springer Nature Switzerland AG 2019 D

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書目名稱Handbook of Deep Learning Applications影響因子(影響力)




書目名稱Handbook of Deep Learning Applications影響因子(影響力)學科排名




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書目名稱Handbook of Deep Learning Applications被引頻次




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書目名稱Handbook of Deep Learning Applications讀者反饋




書目名稱Handbook of Deep Learning Applications讀者反饋學科排名

卡死偷電

gene-therapy

Observe

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Studien zur Kommunikationswissenschaftdeling. Deep learning models are efficient feature selectors and therefore work best in high dimension datasets. We present major research challenges in feature extraction and selection using different deep models. Our case studies are drawn from gene expression datasets. Hence we report some of the

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Deep Learning for Document Representation,d sparse. This sparsity and the need to ensure semantic understanding of text documents are the major challenges in text categorization. Deep learning-based approaches provide a fixed length vector in a continuous space to represent words and documents. This chapter reviews the available document re

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Phase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism ofcene captured by the laparoscopic camera poses additional challenges. These challenges can be overcome by using temporal features in addition to the spatial visual features. A long short-term memory (LSTM) network is used to learn the temporal information of the video. The m2cai16-workflow dataset c

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字形刻痕

Binge-Drinking

Valentina Emilia Balas,Sanjiban Sekhar Roy,Pijush Provides a concise and structured presentation of deep learning applications.Introduces a large range of applications related to vision, speech, and natural language processing.Includes active researc