Abstract
This chapter discusses some of the trends in deep learning and related fields. We cover specifically which trends might be useful for what tasks as well as discuss some of the methods and ideas that could have far-reaching implications but have yet to be applied to many real-world problems. We finish by covering briefly some of the current limitations of deep learning as well as some other areas of AI that seem to hold promise for future AI applications, and discuss briefly some of the ethical and legal implications of deep learning applications.
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- 1.
Lighthill released a report in 1973 that suggested AI was a failure and too superficial to be used in practice, leading to a massive reduced interest in the field.
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Frameworks such as MXNet/Gluon, PyTorch, and Chainer support these types of networks, and we expect this trend to continue.
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© 2018 Mathew Salvaris, Danielle Dean, Wee Hyong Tok
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Salvaris, M., Dean, D., Tok, W.H. (2018). Trends in Deep Learning. In: Deep Learning with Azure. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-3679-6_3
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DOI: https://doi.org/10.1007/978-1-4842-3679-6_3
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-3678-9
Online ISBN: 978-1-4842-3679-6
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