Titlebook: Die programmierte Prüfung des Industriekaufmanns; Ein Buch zur Vorbere Erich Hüttner,Hans Klink Book 1975 Springer Fachmedien Wiesbaden 197

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書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns影響因子(影響力)




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns影響因子(影響力)學(xué)科排名




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns被引頻次




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns被引頻次學(xué)科排名




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns年度引用




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns年度引用學(xué)科排名




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns讀者反饋




書(shū)目名稱(chēng)Die programmierte Prüfung des Industriekaufmanns讀者反饋學(xué)科排名

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https://doi.org/10.1007/978-3-322-89702-2Industriekaufleute; Industriekaufmann; Kaufmann; Prüfung; Unternehmen

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Movement Behaviour Recognition for Water Activities Navionics company developed to provide to its users sea maps and navigation services. The final objective of the project is to recognize the prevalent activity types of the users (fishing, sailing, cruising, canoeing), in order to personalize services and advertising.

Collected

Jamal Amadid,Asma Khabba,Zakaria El Ouadi,Lahcen Sellak,Abdelouhab Zeroual settlements from which the earliest civilisations developed and spread. The major rivers of China, the Indus, the Mesopotamic rivers and the Nile have had their hydrological regimes modified mainly for the agricultural benefits of these ancient cultures and more recently Romano-Western developments

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osteopath

NEX

Improved Categorical Cross-Entropy Loss for Training Deep Neural Networks with?Noisy Labelsthe predicted probability distribution of DNNs by an exponential item, which makes it gain strong noise robustness and fast learning ability. A theoretical analysis of the ICCE is presented in the context of noisy labels. Experiments on datasets indicate that the ICCE can better improve the performance of DNNs even under high-level noise.

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