How are CNNs being used to solve real-world problems and create positive impact?

In the study, there are three primary contributions: First, we proposed a classification model to cultivate the performance of classifying of flower images by ... Nov 14, 2023 ... A Convolutional Neural Network (CNN), also known as ConvNet, is a specialized type of deep learning algorithm mainly designed for tasks that necessitate object ... 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 ... May 12, 2022 ... Enroll for free in the below link to get all the videos and materials https://ineuron.ai/course/Deep-Learning-Foundations Live Deep Learning ... 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 ... Abstract. We present Point-Voxel CNN (PVCNN) for efficient, fast 3D deep learning. Previous work processes 3D data using either voxel-based or point-based NN ... 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 ... Jun 28, 2021 ... Fast R-CNN brings feature extraction and region selection into a single machine learning model. Fast R-CNN receives an image and a set of ... 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 ... Here, the first deep learning (DL) convolutional neural network (CNN) segmentation of multiclass microscale damage in heterogeneous bulk materials is ...