Understanding Simulated Annealing: Definition, Explanations, Examples & Code
Simulated Annealing is an optimization algorithm inspired by the annealing process in metallurgy, which involves heating and controlled cooling of a material. It is used to find the global optimum in a large search space. It uses a random search strategy that accepts new solutions, even those worse than the current solution, based on a probability that decreases as the metaphorical 'temperature' decreases. This ability
Understanding Stacked Auto-Encoders: Definition, Explanations, Examples & Code
Stacked Auto-Encoders is a type of neural network used in Deep Learning. It is made up of multiple layers of sparse autoencoders, with the outputs of each layer connected to the inputs of the next layer. Stacked Auto-Encoders can be trained using unsupervised or semi-supervised learning methods, making it a powerful tool for machine learning engineers to use in their work.
Stacked Auto-Encoders: Introduction
Do
Understanding Stacked Generalization: Definition, Explanations, Examples & Code
Stacked Generalization is an ensemble learning method used in supervised learning. It is designed to reduce the biases of estimators and is accomplished by combining them.
Stacked Generalization: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Ensemble
Stacked Generalization, also known as Stacking, is an ensemble learning method that involves combining multiple base estimators t
Understanding State-Action-Reward-State-Action: Definition, Explanations, Examples & Code
SARSA (State-Action-Reward-State-Action) is a temporal difference on-policy algorithm used in reinforcement learning to train a Markov decision process model on a new policy. This algorithm falls under the category of reinforcement learning, which focuses on how an agent should take actions in an environment to maximize a cumulative reward signal.
State-Action-Reward-State-Action: Introduction
Domain
Understanding Stepwise Regression: Definition, Explanations, Examples & Code
Stepwise Regression is a regression algorithm that falls under the category of supervised learning. It is a method of fitting regression models in which the choice of predictive variables is carried out automatically.
Stepwise Regression: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised
Regression
Stepwise Regression is a regression algorithm used in supervised learning that automati
Understanding Support Vector Machines: Definition, Explanations, Examples & Code
Support Vector Machines (SVM), is an instance-based, supervised learning algorithm used for classification. The algorithm finds the hyperplane that maximizes the margin between classes in the training data. In other words, SVM is a classifier that separates the data points of different classes by drawing a decision boundary or hyperplane in a high-dimensional space. This decision boundary is chosen in such a way th
Understanding Support Vector Regression: Definition, Explanations, Examples & Code
Support Vector Regression (SVR) is an instance-based, supervised learning algorithm which is an extension of Support Vector Machines (SVM) for regression problems. SVR is a powerful technique used in machine learning for predicting continuous numerical values. Unlike traditional regression algorithms, SVR uses support vectors to map data points into a high-dimensional feature space in order to capture non-linear
Understanding t-Distributed Stochastic Neighbor Embedding: Definition, Explanations, Examples & Code
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a popular machine learning algorithm for dimensionality reduction. It is based on the concept of Stochastic Neighbor Embedding and is primarily used for visualization. t-SNE is an unsupervised learning method that maps high-dimensional data to a low-dimensional space, making it easier to visualize clusters and patterns in the data.
t-Distr
Understanding Weighted Average: Definition, Explanations, Examples & Code
The Weighted Average algorithm is an ensemble method of calculation that assigns different levels of importance to different data points. It can be used in both supervised learning and unsupervised learning scenarios.
Weighted Average: Introduction
Domains
Learning Methods
Type
Machine Learning
Supervised, Unsupervised
Ensemble
The Weighted Average algorithm is a powerful calculation method that assigns diff