How can we design CNNs that are both accurate and efficient?

Augmented graph representations are further input to a custom CNN deep learning architecture with a split of 70% train, 10% validation and 20% test ... Dec 7, 2017 ... While people usually tend to simply resize any image into a square while training a CNN (for example, resnet takes a 224x224 square image), that looks ugly to ... A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep ... Nov 26, 2015 ... The field of machine learning has taken a dramatic twist in recent times, with the rise of the Artificial Neural Network (ANN). These ... Complex problems can be solved and objects can be recognized in images based on the Google TensorFlow ™ library - a widely used deep learning framework. Based ... Apr 12, 2023 ... This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state ( ... 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. We believe this suggests significant avenues of future exploration for machine learning language design, pedagogy, and implementation, both inside and outside ... Here, the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale damage in heterogeneous bulk materials is ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data.