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The Interplay of Errors and Weights in Image Recognition by Artificial Neural Networks
Published Oct 12, 2024
Updated Oct 12, 2024
AI
3 min
AI
Published Oct 12, 2024
Updated Oct 12, 2024
3 min
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The Interplay of Errors and Weights in Image Recognition by Artificial Neural Networks
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Optimizing Accuracy and Performance.
Artificial Neural Networks (ANNs) have brought a revolutionary advancement in the image recognition feature of the machine, making it possible for the machines to do almost an expert level perfect jobs for identifying and categorizing images. However, their effectiveness during image recognition is anchored on the ability to manage errors and weights of an ANN. While errors describe misinterpretation of images, weights define strength between individual synapses of neurons in the network. This article looks at the deep meaning of errors and weights in relation to ANNs’ image recognition and explains all sorts of errors, the extent of impact that weights have on performance, and how to perfect both definitively.
This as an inherent characteristic of ANNs means that errors can happen, for a range of reasons such as – noise in the data, insufficient training or over-training.
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