FastSpeech 2
FastSpeech 2: Improving Text-to-Speech Technology
Text-to-speech (TTS) technology has greatly improved in recent years, but there is still a major challenge it faces called the one-to-many mapping problem. This refers to the issue where multiple speech variations correspond to the…
G3D
G3D is a new method for modeling spatial-temporal data that allows for direct joint analysis of space and time. Essentially, this means that it takes both spatial and temporal information into account when analyzing data, which can be useful in…
Bilateral Guided Aggregation Layer
What is Bilateral Guided Aggregation Layer?
Bilateral Guided Aggregation Layer is a technique that is used in the field of computer vision to improve semantic segmentation. It is a feature fusion layer that aims to bring together different types of…
TrOCR
Overview of TrOCR
TrOCR is a cutting-edge OCR (Optical Character Recognition) model that uses pre-trained models for both CV (Computer Vision) and NLP (Natural Language Processing) to recognize and generate text from images. It utilizes the Transformer architecture to decipher…
Deep Voice 3
Deep Voice 3: A Revolutionary Text-to-Speech System
If you're looking for an advanced text-to-speech system that offers high-quality audio output, then Deep Voice 3 (DV3) may be just what you're looking for. DV3 is an attention-based neural text-to-speech system that…
k-Nearest Neighbor
Understanding k-Nearest Neighbor: Definition, Explanations, Examples & Code
The k-Nearest Neighbor (kNN) algorithm is a simple instance-based algorithm used for both supervised and unsupervised learning. It stores all the available cases and classifies new cases based on a similarity measure. The…
Simulated Annealing
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…
Particle Swarm Optimization
Understanding Particle Swarm Optimization: Definition, Explanations, Examples & Code
Particle Swarm Optimization (PSO) is an optimization algorithm inspired by the social behavior of birds and fish. It operates by initializing a swarm of particles in a search space, where each particle…
Rotation Forest
Understanding Rotation Forest: Definition, Explanations, Examples & Code
Rotation Forest is an ensemble learning method that generates individual decision trees based on differently transformed subsets of the original features. The transformations aim to enhance diversity among the individual models, increasing the…
Asynchronous Advantage Actor-Critic
Understanding Asynchronous Advantage Actor-Critic: Definition, Explanations, Examples & Code
The Asynchronous Advantage Actor-Critic (A3C) algorithm is a deep reinforcement learning method that uses multiple independent neural networks to generate trajectories and update parameters asynchronously. It involves two models: an actor, which…
Affinity Propagation
Understanding Affinity Propagation: Definition, Explanations, Examples & Code
The Affinity Propagation (AP) algorithm is a type of unsupervised machine learning algorithm used for clustering. It automatically determines the number of clusters and operates by passing messages between pairs of samples until…
Density-Based Spatial Clustering of Applications with Noise
Understanding Density-Based Spatial Clustering of Applications with Noise: Definition, Explanations, Examples & Code
The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is a clustering algorithm used in unsupervised learning. It groups together points that are densely packed (i.e. points…
Actor-critic
Understanding Actor-critic: Definition, Explanations, Examples & Code
Actor-critic is a temporal difference algorithm used in reinforcement learning.
It consists of two networks: the actor, which decides which action to take, and the critic, which evaluates the action produced by the actor…
Policy Gradients
Understanding Policy Gradients: Definition, Explanations, Examples & Code
Policy Gradients (PG) is an optimization algorithm used in artificial intelligence and machine learning, specifically in the field of reinforcement learning. This algorithm operates by directly optimizing the policy the agent is using,…
Label Propagation Algorithm
Understanding Label Propagation Algorithm: Definition, Explanations, Examples & Code
The Label Propagation Algorithm (LPA) is a graph-based semi-supervised machine learning algorithm that assigns labels to previously unlabeled data points. LPA works by propagating labels from a subset of data points that…
Label Spreading
Understanding Label Spreading: Definition, Explanations, Examples & Code
The Label Spreading algorithm is a graph-based semi-supervised learning method that builds a similarity graph based on the distance between data points. The algorithm then propagates labels throughout the graph and uses this…
LightGBM
Understanding LightGBM: Definition, Explanations, Examples & Code
LightGBM is an algorithm under Microsoft's Distributed Machine Learning Toolkit. It is a gradient boosting framework that uses tree-based learning algorithms. It is an ensemble type algorithm that performs supervised learning. LightGBM is designed…
CatBoost
Understanding CatBoost: Definition, Explanations, Examples & Code
Developed by Yandex, CatBoost (short for "Category" and "Boosting") is a machine learning algorithm that uses gradient boosting on decision trees. It is specifically designed to work effectively with categorical data by transforming categories…
eXtreme Gradient Boosting
Understanding eXtreme Gradient Boosting: Definition, Explanations, Examples & Code
XGBoost, short for eXtreme Gradient Boosting, is a popular machine learning algorithm that employs the gradient boosting framework. It leverages decision trees as base learners and combines them to produce a final,…
State-Action-Reward-State-Action
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…
Latent Dirichlet Allocation
Understanding Latent Dirichlet Allocation: Definition, Explanations, Examples & Code
Latent Dirichlet Allocation (LDA) is a Bayesian generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. It…
t-Distributed Stochastic Neighbor Embedding
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…
Isolation Forest
Understanding Isolation Forest: Definition, Explanations, Examples & Code
Isolation Forest is an unsupervised learning algorithm for anomaly detection that works on the principle of isolating anomalies. It is an ensemble type algorithm, which means it combines multiple models to improve performance.…
Support Vector Regression
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…
Semi-Supervised Support Vector Machines
Understanding Semi-Supervised Support Vector Machines: Definition, Explanations, Examples & Code
Semi-Supervised Support Vector Machines (S3VM) is an extension of Support Vector Machines (SVM) for semi-supervised learning. It is an instance-based algorithm that makes use of a large amount of unlabelled data…
Mini-Batch Gradient Descent
Understanding Mini-Batch Gradient Descent: Definition, Explanations, Examples & Code
Mini-Batch Gradient Descent is an optimization algorithm used in the field of machine learning. It is a variation of the gradient descent algorithm that splits the training dataset into small batches. These…
Gradient Descent
Understanding Gradient Descent: Definition, Explanations, Examples & Code
Gradient Descent is a first-order iterative optimization algorithm used to find a local minimum of a differentiable function. It is one of the most popular algorithms for machine learning and is used in…
Differential Evolution
Understanding Differential Evolution: Definition, Explanations, Examples & Code
Differential Evolution is an optimization algorithm that aims to improve a candidate solution iteratively with respect to a defined quality measure. It belongs to the family of evolutionary algorithms and is widely used…
Genetic
Understanding Genetic: Definition, Explanations, Examples & Code
The Genetic algorithm is a type of optimization algorithm that is inspired by the process of natural selection, and is considered a heuristic search and optimization method. It is a popular algorithm in the…
Boosting
Understanding Boosting: Definition, Explanations, Examples & Code
Boosting is a machine learning ensemble meta-algorithm that falls under the category of ensemble learning methods and is mainly used to reduce bias and variance in supervised learning.
Boosting: Introduction
Domains
Learning Methods
Type…
Bootstrapped Aggregation
Understanding Bootstrapped Aggregation: Definition, Explanations, Examples & Code
Bootstrapped Aggregation is an ensemble method in machine learning that improves stability and accuracy of machine learning algorithms used in statistical classification and regression. It is a supervised learning technique that builds multiple…
AdaBoost
Understanding AdaBoost: Definition, Explanations, Examples & Code
AdaBoost is a machine learning meta-algorithm that falls under the category of ensemble methods. It can be used in conjunction with many other types of learning algorithms to improve performance.
AdaBoost uses supervised learning…
Weighted Average
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…
Stacked Generalization
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…
Gradient Boosting Machines
Understanding Gradient Boosting Machines: Definition, Explanations, Examples & Code
The Gradient Boosting Machines (GBM) is a powerful ensemble machine learning technique used for regression and classification problems. It produces a prediction model in the form of an ensemble of weak prediction…
Gradient Boosted Regression Trees
Understanding Gradient Boosted Regression Trees: Definition, Explanations, Examples & Code
The Gradient Boosted Regression Trees (GBRT), also known as Gradient Boosting Machine (GBM), is an ensemble machine learning technique used for regression problems.
This algorithm combines the predictions of multiple decision…
Random Forest
Understanding Random Forest: Definition, Explanations, Examples & Code
Random Forest is an ensemble machine learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the…
Principal Component Analysis
Understanding Principal Component Analysis: Definition, Explanations, Examples & Code
Principal Component Analysis (PCA) is a type of dimensionality reduction technique in machine learning that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set…
Principal Component Regression
Understanding Principal Component Regression: Definition, Explanations, Examples & Code
Principal Component Regression (PCR) is a dimensionality reduction technique that combines Principal Component Analysis (PCA) and regression. It first extracts the principal components of the predictors and then performs a linear regression…
Partial Least Squares Regression
Understanding Partial Least Squares Regression: Definition, Explanations, Examples & Code
Partial Least Squares Regression (PLSR) is a dimensionality reduction technique used in supervised learning. PLSR is a method for constructing predictive models when the factors are many and highly collinear. It…
Sammon Mapping
Understanding Sammon Mapping: Definition, Explanations, Examples & Code
Sammon Mapping is a non-linear projection method used in dimensionality reduction. It belongs to the unsupervised learning methods and aims to preserve the structure of the data as much as possible in lower-dimensional…
Multidimensional Scaling
Understanding Multidimensional Scaling: Definition, Explanations, Examples & Code
Multidimensional Scaling (MDS) is a dimensionality reduction technique used in unsupervised learning. It is a means of visualizing the level of similarity of individual cases of a dataset in a low-dimensional space.
Multidimensional…
Projection Pursuit
Understanding Projection Pursuit: Definition, Explanations, Examples & Code
Projection Pursuit is a type of dimensionality reduction algorithm that involves finding the most "interesting" possible projections in multidimensional data. It is a statistical technique that can be used for various purposes, such…
Mixture Discriminant Analysis
Understanding Mixture Discriminant Analysis: Definition, Explanations, Examples & Code
Mixture Discriminant Analysis (MDA) is a dimensionality reduction method that extends linear and quadratic discriminant analysis by allowing for more complex class conditional densities. It falls under the category of supervised learning…
Quadratic Discriminant Analysis
Understanding Quadratic Discriminant Analysis: Definition, Explanations, Examples & Code
Quadratic Discriminant Analysis (QDA) is a dimensionality reduction algorithm used for classification tasks in supervised learning. QDA generates a quadratic decision boundary by fitting class conditional densities to the data and using…
Flexible Discriminant Analysis
Understanding Flexible Discriminant Analysis: Definition, Explanations, Examples & Code
The Flexible Discriminant Analysis (FDA), also known as FDA, is a dimensionality reduction algorithm that is a generalization of linear discriminant analysis. Unlike the traditional linear discriminant analysis, FDA uses non-linear combinations…
Convolutional Neural Network
Understanding Convolutional Neural Network: Definition, Explanations, Examples & Code
Convolutional Neural Network (CNN), a class of deep neural networks, is widely used in pattern recognition and image processing tasks. CNNs can also be applied to any type of input that can…
Recurrent Neural Network
Understanding Recurrent Neural Network: Definition, Explanations, Examples & Code
The Recurrent Neural Network, also known as RNN, is a type of Deep Learning algorithm. It is characterized by its ability to form directed graph connections between nodes along a sequence, which…
Long Short-Term Memory Network
Understanding Long Short-Term Memory Network: Definition, Explanations, Examples & Code
The Long Short-Term Memory Network (LSTM) is a type of deep learning algorithm capable of learning order dependence in sequence prediction problems. As a type of recurrent neural network, LSTM is…
Stacked Auto-Encoders
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…