What is the role of a loss function in training CNNs?

Aug 1, 2019 ... ... CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object ... Jun 27, 2017 ... The convolution in a CNN is going to be the kernel g of which we will learn some weights in order to extract the information we need and then perhaps apply an ... ... CNN; Deep neural network; RT-PCR; Computer model. Received: October 27, 2022 ... Machine Learning With Applications, 6, 100138. https://doi.org/10.1016 ... ... CNN deep feature extraction with machine learning models to classify variety of food image datasets. Date of Award, Oct 2018. Original language, English. 3 From CNN to Faster R-CNN. Deep learning (DL) is a subfield of machine learning which attempts to acquire high-level abstractions in data by utilising ... We have proposed a deep learning method called BCM-SegNet, an optimized Mask R-CNN-based algorithm for segmenting BSE images of bronze casting moulds and cores. A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production. Electric Power Systems Research, 2022, 208 ... We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not ... Apr 17, 2020 ... Convolution in Convolutional Neural Networks. The convolutional neural network, or CNN for short, is a specialized type of neural network model ...