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

expenditure

書目名稱Advanced Deep Learning for Engineers and Scientists影響因子(影響力)




書目名稱Advanced Deep Learning for Engineers and Scientists影響因子(影響力)學科排名




書目名稱Advanced Deep Learning for Engineers and Scientists網(wǎng)絡(luò)公開度




書目名稱Advanced Deep Learning for Engineers and Scientists網(wǎng)絡(luò)公開度學科排名




書目名稱Advanced Deep Learning for Engineers and Scientists被引頻次




書目名稱Advanced Deep Learning for Engineers and Scientists被引頻次學科排名




書目名稱Advanced Deep Learning for Engineers and Scientists年度引用




書目名稱Advanced Deep Learning for Engineers and Scientists年度引用學科排名




書目名稱Advanced Deep Learning for Engineers and Scientists讀者反饋




書目名稱Advanced Deep Learning for Engineers and Scientists讀者反饋學科排名

婚姻生活

嘲笑

nephritis

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.

biopsy

flaunt

植物茂盛

Diskussion der Mobilit?tsarrangementsovering 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.

entitle

愛哭

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.

intellect

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