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- W4230201859 abstract "Activation functionsActivation functions lie at the core of deep neural networksLearning deep neural networks allowing them to learn arbitrarily complex mappings. Without any activation, a neural network learn will only be able to learn a linear relation between input and the desired output. The chapter introduces the reader to why activation functionsActivation functions are useful and their immense importance in making deep learning successful. A detailed survey of several existing activation functionsActivation functions is provided in this chapter covering their functional forms, original motivations, merits as well as demerits. The chapter also discusses the domain of learnable activation functionsActivation functions and proposes a novel activation ‘SLAF’ whose shape is learned during the training of a neural network. A working model for SLAFSLAF is provided and its performance is experimentally shown on XOR and MNIST classificationClassification tasks." @default.
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- W4230201859 date "2019-10-24" @default.
- W4230201859 modified "2023-09-26" @default.
- W4230201859 title "Activation Functions" @default.
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- W4230201859 doi "https://doi.org/10.1007/978-3-030-31760-7_1" @default.
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