How can we build more reliable and trustworthy AI systems based on CNNs?

Jan 22, 2025 ... A breakthrough in building models for image classification came with the discovery that a convolutional neural network (CNN) could be used ... (Not recommended) Train R-CNN deep learning object detector. collapse all in ... This function requires that you have Deep Learning Toolbox™ and Statistics and ... Current approaches to object recognition make essential use of machine learning methods. To im- prove their performance, we can collect larger datasets, learn ... ... intelligence must solve. One of the solutions is to apply deep learning and computer vision. Convolutional Neural Networks' (CNN) deep architecture ... Feb 10, 2020 ... The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models ... 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. Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test ... May 12, 2022 ... Comments94 · Day 1- Live Deep Learning Community Session · Day 3- Optimizers And ANN Implementation| Live Deep Learning Community Session. Sep 30, 2024 ... I'm a CS student and have already studied and implemented "normal" Neural Networks, as well as many other machine learning algorithms, so I have ... A deep neural network (DNN) is an artificial neural network with multiple hidden layers of units between the input and output layers.