What are the trade-offs between model accuracy and interpretability?

Feb 23, 2023 ... The CNN and recurrent including LSTM and GRU are all supported on GPU using the native Deep Learning package. Feb 20, 2020 ... LeNet-5. LeNet-5 architecture is perhaps the most widely known CNN architecture. It was created by Yann LeCun in 1998 and widely used for ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current ... The basic CNN architecture consists of five main layers: input, convolutional, activation, pooling, and fully connected. Each layer plays a specific role in ... Layer-Wise Data-Free CNN Compression ... We present an efficient method for compressing a trained neural network without using any data. Our data-free method ... Jan 30, 2023 ... Abstract. In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural ... Mar 25, 2023 ... In this paper, we compared and explored the two methodologies on the DEMoS dataset consisting of 8869 audio files of 58 speakers in different emotional states. CNN is one of the popular deep learning algorithms that recently gained popularity in the sector of hydrology and earth sciences (Panahi et al., 2020). The CNN ... This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles ...