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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978-3-030-66521-0The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Advanced Deep Learning for Engineers and Scientists978-3-030-66519-7Series ISSN 2522-8595 Series E-ISSN 2522-8609

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M. Morlock,V. Bonin,E. Schneider, audio recognition, bioinformatics, and automatic driving of vehicles due to its potential benefits such as feature extraction and data classification problems. It is an evolving research domain in diverse applications which increases the overall potential cost benefits for maintenance and refurbis

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https://doi.org/10.1007/978-3-642-72235-6ition to the smart grid domain, deep learning has proved itself to be an effective tool in producing state-of-the-art results. This chapter discusses how the usage of a high-level language like Python and its compatibility with deep learning frameworks and its collection of utility libraries facilit

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R. Ellegast,J. Kupfer,D. Reinertapability of executing the same code in various backends. It is written in Python, which executes on Theano or TensorFlow. It is observed from various studies that Keras has extensive usage in various structured and unstructured domains. The objective of this chapter is to provide an overview of Ker

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https://doi.org/10.1007/978-3-642-72235-6m of smartphones. Google Lens, Siri, Alexa, and many other AI assistances had become part of our lives now. The key features like identifying an image or text is a necessity for such programs; in fact many investments are being poured in building better models for image recognition and contextual sp