Dr. James McCaffrey of Microsoft Research says a neural network model is arguably the most powerful multi-class classification technique.

This project showcases my understanding of image classification through the implementation of different neural network architectures. I have built models using PyTorch and NumPy to classify images, ...

This project focuses on building a neural network model for image classification. The goal is to accurately classify images into predefined categories using deep learning. By training on a dataset of ...

Comparative Analysis of Custom Neural Network Model and VGG-16 for Multi-class Image Classification Abstract: This study assesses the performance of CustomNet, a lightweight neural network model ...

In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ...

Following new best practices, Dr. James McCaffrey of Microsoft Research revisits multi-class classification for when the variable to predict has three or more possible values.

Neural network architectures, particularly deep learning models, have revolutionised this field by enabling the effective extraction of complex spectral–spatial features.

Implementing Multi-Class Classification Using Mobilenet_v2 We are using a pre-trained model called MobileNet_v2, which is a popular network for image-based classification, and trained on 1000 classes ...

This study assesses the performance of CustomNet, a lightweight neural network model trained using NumPy and Pandas, compared to the VGG-16 architecture on the datasets of MNIST, Fashion MNIST, and ...