When using the scikit library for multi-class classification, the main alternative to the MLPClassifier neural network module is the scikit DecisionTree module. Decision trees are useful for ...

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 ...

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 ...

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 ...

When saving or loading a trained model, the model should be in eval() mode rather than train() mode. An alternative approach for saving a PyTorch model is to use ONNX (Open Neural Network Exchange).

Hyperspectral image classification has emerged as a transformative technology in Earth observation, providing unprecedented detail through hundreds of contiguous spectral bands. Neural network ...

Innovation of deep neural networks has given rise to many AI-based applications and overcome the difficulties faced by computer vision-based applications ... Implementing Multi-Class Classification ...

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 ...