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Deep Learning (DL) techniques have evolved and increased in power in recent years. Tens of hundreds of algorithms and applications have evolved and exhibit tremendous possibilities in various domains such as scientific research, agriculture, smart cities, finance, healthcare, conservation, the environment, industry and more. Innovative ideas using appropriate DL framework, metrics, and methods is improving the productivity of a large sector of researchers and practitioners and are now actively employed for the development and impact on smart cities and societies. This book highlights the importance of specific frameworks such as IoT enabled frameworks or serverless cloud frameworks that are applying DL techniques for solving these persistent societal problems. It addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modelling different type of data. In particular the book explores and emphasises techniques involved in deep learning such as image classification, image enhancement, word analysis, human-machine emotional interfaces and the applications of these techniques for smart cities and societal problems. To extend a theoretical description the book is enhanced through case studies, including those implemented using tensorflow2 and relevant IoT specific sensor/actuator frameworks.
The broad coverage will be essential reading to not just to advanced students and academic researchers but also to practitioners and engineers in a range of disciplines.
