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ADABOOST


ADADELTA


ADABOOST


ADADELTA


ADABOOST


ADADELTA


Latest posts
Text-to-Speech Models


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…


Action Recognition Blocks


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…


Semantic Segmentation Modules


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…


OCR Models


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…


Sequence To Sequence Models Text-to-Speech Models


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…


Machine Learning Algorithms Supervised Learning Unsupervised Learning
Semi-supervised Learning


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…


Machine Learning Algorithms


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…


Machine Learning Algorithms


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Reinforcement Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Reinforcement Learning Machine Learning Actor-Critic
Algorithms


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…


Machine Learning Algorithms Reinforcement Learning


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,…


Machine Learning Algorithms Semi-supervised Learning


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…


Machine Learning Algorithms Semi-supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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,…


Machine Learning Algorithms Reinforcement Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Unsupervised Learning


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.…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Semi-supervised Learning


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…


Machine Learning Algorithms


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…


Machine Learning Algorithms


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…


Machine Learning Algorithms


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…


Machine Learning Algorithms


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning Machine Learning


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…


Machine Learning Algorithms Supervised Learning Unsupervised 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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Unsupervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms 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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Supervised Learning Unsupervised Learning


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…


Machine Learning Algorithms Supervised Learning


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…


Machine Learning Algorithms Unsupervised Learning Semi-supervised Learning


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…


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