As a result, we are presenting a state-of-art deep learning solution targeted not ... In this study, we will propose our model architecture, which involves a convolutional neural network (CNN) to ...

Note: This project is mostly an application for masonry historic structures using fine-tuned deep learning algorithms. If you are more interested in the CNN architecture or implementation ... 1: ...

Now, to build your own Neural Network, try completing this short course by Andrew NG - Neural Networks and Deep Learning. You can opt for Financial ... start with understanding the significance of CNN ...

Added cnn and fc layer with images. Created a [cnn-pool-cnn-pool-fc-softmax] architecture and got 67% accuracy (overfitting?) Currently trying to accomodate the transfer learning by adapting to ...

The project "Identification of Medicinal Plants using CNN" leverages deep learning ... By incorporating transfer learning with MobileNet, we harness the power of proven deep learning architecture, ...

This example works with the TUH Abnormal EEG Corpus, which is an expert-labeled dataset suited for supervised learning. This example seeks ... this example uses a deep CNN architecture that allows EEG ...

Keras implementation of CNN, DeepConvLSTM, and SDAE and LightGBM for sensor-based Human Activity Recognition (HAR). This repository contains keras (tensorflow.keras) implementation of Convolutional ...

Welcome to the "Fashion MNIST CNN Classifier" project ... demonstrating our ability to effectively leverage deep learning techniques to solve complex problems in the fashion domain. Our primary ...

Inspired by Machine-Learning-Tokyo/CNN-Architectures, this repo is to show how to implement SOTA point cloud deep learning-based networks with the tensorflow.keras ...

Using smartphone sensors and a hybrid deep learning model, researchers developed a non-invasive method to detect obesity in ...

Improvement of VGG-16 with TL 72.5% 20 Tune more HT Improvement of VGG-16 with TL+FT 80.8% 25 Tune more HT ...

A study in Discover Applied Sciences demonstrated the use of deep learning models, specifically convolutional neural networks ...