Abstract: The present research examines the efficacy of various neural network methodologies for digit classification using the MNIST dataset, encompassing a fundamental Neural Network (Sequential API ...

What if you could turn Excel into a powerhouse for advanced data analysis and automation in just a few clicks? Imagine effortlessly cleaning messy datasets, running complex calculations, or generating ...

This hands-on tutorial will walk you through the entire process of working with CSV/Excel files and conducting exploratory data analysis (EDA) in Python. We’ll use a realistic e-commerce sales dataset ...

In this study, we propose a novel modularized Quantum Neural Network (mQNN) model tailored to address the binary classification problem on the MNIST dataset. The mQNN organizes input information using ...

In view of the growing volume of data, there is a notable research focus on hardware that offers high computational performance with low power consumption. Notably, neuromorphic computing, ...

Natural neural systems have inspired innovations in machine learning and neuromorphic circuits designed for energy-efficient data processing. However, implementing the backpropagation algorithm, a ...

Abstract: In this work, two different deep learning architectures Residual Network (ResNet) and VGG Network are implemented for the MNIST digit recognition challenge. With a changed architecture, the ...

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