Abstract
Artificial intelligence (AI) has been a buzz word for quite a long time now; the advancements in this field have reached its way to our pockets in form 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 speed recognition through neural networks. In this chapter, the architectures of various neural networks are explored. Fundamentally, convolution neural 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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Bharadwaj, Y.S.S. (2021). Advanced Deep Learning Techniques. In: Prakash, K.B., Kannan, R., Alexander, S., Kanagachidambaresan, G.R. (eds) Advanced Deep Learning for Engineers and Scientists. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-66519-7_6
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