Titlebook: Advanced Deep Learning for Engineers and Scientists; A Practical Approach Kolla Bhanu Prakash,Ramani Kannan,G. R. Kanagachid Book 2021 The

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书目名称Advanced Deep Learning for Engineers and Scientists影响因子(影响力)学科排名




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书目名称Advanced Deep Learning for Engineers and Scientists网络公开度学科排名




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书目名称Advanced Deep Learning for Engineers and Scientists被引频次学科排名




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书目名称Advanced Deep Learning for Engineers and Scientists年度引用学科排名




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书目名称Advanced Deep Learning for Engineers and Scientists读者反馈学科排名

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R. Ellegast,J. Kupfer,D. Reinert studies that Keras has extensive usage in various structured and unstructured domains. The objective of this chapter is to provide an overview of Keras, its models and layers. Classification (binary/multi-class) and regression models are discussed with help of case studies using Keras.

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Diskussion der Mobilitätsarrangementsovering hidden information and making correct predictions, and bioinformatics is no exception. In this review, potential applications of deep learning in bioinformatics research such as genomic sequence analysis, protein structure prediction, biomedical image processing and other omics data analyses have been presented.

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https://doi.org/10.1007/978-3-642-72235-6 networks aka ConvNets or CNN and recurrent neural networks or RNN are explained with a few examples and their implementation in Python. Intuitive explanation with easily understandable mathematical interpretation can be seen in this chapter.

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https://doi.org/10.1007/978-3-030-66519-7Deep Learning; Autoencoder; Pytorch and Deep Learning; Keras and Deep Learning; Deep dream; Tensorflow; Ne