Activation Functions in Neural Networks: Detailed Explanation Activation functions play a crucial role in neural networks, enabling them to capture non-linear relationships in the data. Without activation functions, a neural….
Forward and Backpropagation in Neural Networks: Detailed Explanation Forward and backward propagation are two key steps in the training process of a neural network. These steps are fundamental to how….
Introduction to Neural Networks: Detailed Explanation A Neural Network (NN) is a computational model inspired by the way biological neural networks in the human brain process information. It is a….
Self-Organizing Maps (SOMs): Detailed Explanation Self-Organizing Maps (SOMs), also known as Kohonen maps, are a type of unsupervised neural network developed by Teuvo Kohonen in the 1980s. SOMs are primarily….
Anomaly Detection – A Comprehensive Guide 1. Introduction to Anomaly Detection Anomaly Detection is the process of identifying rare or unusual patterns in data that do not conform to expected….
Hierarchical Clustering: A Comprehensive Guide 1. Introduction to Hierarchical Clustering Hierarchical Clustering is an unsupervised machine learning algorithm used to group similar objects into clusters. Unlike K-Means, it does not….
Gradient Boosting (XGBoost, LightGBM, CatBoost) in Machine Learning 1. Introduction to Gradient Boosting Gradient Boosting is a powerful ensemble learning technique used in machine learning for classification and regression tasks…..
