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Titlebook: Business Intelligence and Big Data; 7th European Summer Esteban Zimányi Conference proceedings 2018 Springer Nature Switzerland AG 2018 bu

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樓主: choleric
11#
發(fā)表于 2025-3-23 12:08:18 | 只看該作者
,Let’s Open the Black Box of Deep Learning!, what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review what is a neural network, how we can learn its parameters by using observational data, some of the most common architectures (CNN, LSTM, etc.) and some of the tricks that have been developed during the last years.
12#
發(fā)表于 2025-3-23 14:45:18 | 只看該作者
Pluralism of Media Types and Media Genrestadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of metadata via profiling algorithms. In this course, we will discuss the importance of data prof
13#
發(fā)表于 2025-3-23 20:39:14 | 只看該作者
14#
發(fā)表于 2025-3-24 01:37:58 | 只看該作者
15#
發(fā)表于 2025-3-24 05:38:34 | 只看該作者
16#
發(fā)表于 2025-3-24 09:08:32 | 只看該作者
Yolande Stolte,Rachael Craufurd Smitht of techniques for handling and processing such streams of data is very challenging as the streaming context imposes severe constraints on the computation: we are often not able to store the whole data stream and making multiple passes over the data is no longer possible. As the stream is never fin
17#
發(fā)表于 2025-3-24 11:10:55 | 只看該作者
Henrik S?ndergaard,Rasmus Helles what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review what is a neural network, how we can learn its parameters by using observational data, some of the most common architectures (CNN, LSTM, etc.) and some of the tricks that have
18#
發(fā)表于 2025-3-24 18:46:11 | 只看該作者
19#
發(fā)表于 2025-3-24 19:34:11 | 只看該作者
Business Intelligence and Big Data978-3-319-96655-7Series ISSN 1865-1348 Series E-ISSN 1865-1356
20#
發(fā)表于 2025-3-25 02:45:44 | 只看該作者
Henrik S?ndergaard,Rasmus Helles what are the real mechanisms that make this technique a breakthrough with respect to the past. To this end, we will review what is a neural network, how we can learn its parameters by using observational data, some of the most common architectures (CNN, LSTM, etc.) and some of the tricks that have been developed during the last years.
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