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- W3204690939 abstract "Deep learning is based on a network of artificial neurons inspired by the human brain. This network is made up of tens or even hundreds of layers of neurons. The fields of application of deep learning are indeed multiple; Agriculture is one of those fields in which deep learning is used in various agricultural problems (disease detection, pest detection, weed identification ...). A major problem with deep learning is how to create a model that works well, not only on the learning set but also on the validation set. Many approaches used in neural networks are explicitly designed to reduce overfit, possibly at the expense of increasing validation accuracy and training accuracy. In this paper, a basic technique (Dropout) is proposed to minimize overfit, we integrated it into a Convolutional Neural Network model to classify weed species and see how it impacts performance, a complementary solution (Exponential Linear Units) are proposed to optimize the obtained results. The results showed that these proposed solutions are practical and highly accurate, enabling us to adopt them in deep learning models." @default.
- W3204690939 created "2021-10-11" @default.
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- W3204690939 date "2021-11-01" @default.
- W3204690939 modified "2023-09-26" @default.
- W3204690939 title "Dropout, a basic and effective regularization method for a deep learning model: a case study" @default.
- W3204690939 hasPublicationYear "2021" @default.
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