Introduction Traffic signs are crucial for ensuring road safety and guiding drivers. With self-driving cars and intelligent transportation systems becoming more common, automating the recognition of traffic signs is vital. This project, “Traffic Sign Classification,” leverages deep learning to identify traffic signs from images,...

sigmoid neuron, deep learning, neural network, activation function, non-linearity, machine learning, backpropagation, ReLU, vanishing gradient problem

In machine learning, an epoch refers to one complete pass through the entire training dataset. During each epoch, the model is exposed to all training examples and updates its parameters (weights and biases) to minimize the error between its predictions and the actual values....

The perceptron is a fundamental concept in machine learning, serving as one of the earliest models for binary classification. This document explores the perceptron algorithm, its architecture, and its significance in the broader context of machine learning. We will delve into how the perceptron...

Hierarchical Clustering: A Top-Down Approach to Grouping Data Hierarchical clustering is a popular unsupervised machine learning technique used to group similar data points together. 1 Unlike other clustering algorithms, it creates a hierarchy of clusters, representing a nested structure. This hierarchical structure can be...

What is SVM? Support Vector Machine (SVM) is a versatile supervised machine learning algorithm that excels in classification and regression tasks. It’s particularly effective when dealing with high-dimensional data and complex decision boundaries. How Does SVM Work? At its core, SVM aims to find...

The District Health Information System 2 (DHIS2) has emerged as a pivotal tool for enhancing the efficiency and effectiveness of health data management in Nepal. As a flexible, open-source platform, DHIS2 empowers healthcare providers, policymakers, and researchers to make informed decisions based on reliable,...