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….
Tag: feature extraction
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….
Autoencoders
Autoencoders: Detailed Explanation Autoencoders are a class of neural networks used for unsupervised learning. Their primary goal is to learn an efficient representation of the input data, typically for the….
t-SNE for High-Dimensional Data
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a powerful technique for dimensionality reduction and visualization of high-dimensional data. Unlike PCA, which is a linear method, t-SNE is a non-linear technique that….
Principal Component Analysis (PCA)
Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction while preserving as much variability in the data as possible. It is widely used in fields such as….
Using Principal Component Analysis (PCA)
Principal Component Analysis (PCA) – A Comprehensive Guide Introduction to PCA Principal Component Analysis (PCA) is a powerful dimensionality reduction technique used in machine learning and data science. It transforms….
Dimensionality Reduction Techniques
Dimensionality Reduction Techniques: A Comprehensive Guide Introduction Dimensionality reduction is a critical step in data preprocessing that helps improve the efficiency and performance of machine learning models by reducing the….
Feature Selection Techniques
Feature Selection Techniques: A Comprehensive Guide Introduction Feature selection is a crucial step in machine learning that involves selecting the most relevant features (variables) for building an efficient and accurate….
Feature Engineering
Feature Engineering: A Comprehensive Guide Introduction Feature engineering is the process of transforming raw data into meaningful features that improve the performance of machine learning models. It involves selecting, creating,….