Semantic Segmentation
Semantic Segmentation: A Comprehensive Guide Introduction to Semantic Segmentation Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image. Unlike object detection,….
Tag: Artificial Intelligence
Introduction to Neural Networks
A Neural Network is a computational model inspired by the human brain. It consists of layers of interconnected neurons that process input data to learn patterns and make predictions. Neural….
Introduction to Neural Networks
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)
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….
Gradient Boosting (XGBoost, LightGBM, CatBoost)
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…..
k-Nearest Neighbors (k-NN)
k-Nearest Neighbors (k-NN) Algorithm in Machine Learning 1. Introduction to k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) is a supervised learning algorithm used for classification and regression tasks. It is a….
Feature Scaling in Machine Learning
Feature Scaling in Machine Learning Introduction Feature scaling is a crucial step in the data preprocessing stage of machine learning. It ensures that all numerical features in the dataset have….
Underfitting vs Overfitting
Underfitting vs Overfitting in Machine Learning Introduction One of the biggest challenges in machine learning is building a model that can generalize well to unseen data. The two common problems….
Bias-Variance Tradeoff
Bias-Variance Tradeoff in Machine Learning Introduction The bias-variance tradeoff is a fundamental concept in machine learning that describes the tradeoff between two sources of error that affect model performance: Understanding….
Train-Test Split and Cross-Validation
Train-Test Split and Cross-Validation in Machine Learning In machine learning, evaluating the performance of a model is crucial to ensure it generalizes well to unseen data. Two widely used techniques….