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What is a Markov Chains? and Applications of Markov Chains
What is a Markov Chains?
Systems that change between several states, according to probabilistic criteria are described by Markov chains, a potent mathematical framework. Markov chains...
What is Hierarchical composition, How it works, and features
Neural networks are designed using the core design idea of hierarchical composition, in which network layers are arranged to learn increasingly abstract and sophisticated properties....
What is sparse decomposition? and its Fundamental Goals
A strong signal processing method with many uses, sparse decomposition is especially useful for feature extraction and denoising.
What is sparse decomposition?
A signal or data...
What are Sparse AutoEncoders, And architecture, advantages
A potent and increasingly well-liked method for deciphering intricate machine learning models, especially Large Language Models (LLMs), is the use of Sparse Autoencoders (SAEs).
What are Sparse...
Advantages and disadvantages of DNs & architecture of DNs
In this article, we can learn what Deconvolutional Networks are, the architecture of DNs, history, Advantages, disadvantages of DNs, challenges, features, and types of...
Stochastic Gradient Variational Bayes and its Advantages
Stochastic Gradient Variational Bayes
Stochastic Gradient Variational Bayes (SGVB) is a potent technique for approximation Bayesian inference that is mostly applied to neural networks. It...
What is Deep Convolutional Inverse Graphics Network(DC-IGN)?
A neural network architecture called the Deep Convolutional Inverse Graphics Network (DC-IGN) was created to extract the underlying three-dimensional structure of sceneries and objects from two-dimensional...
What is Deep Generative Models? and its types
What is Deep Generative Models?
One type of artificial intelligence (AI) and machine learning (ML) model that makes use of deep neural networks is called a deep...
Deep Boltzmann Machines, & What are the advantages of DBMs
A sophisticated kind of artificial neural network that belongs to the generative model family, Deep Boltzmann Machines (DBMs) are made to extract intricate patterns from massive...
Spatial Transformer Networks Advantages and Key Properties
A notable development in deep learning, Spatial Transformer Networks (STNs) are mainly intended to improve the spatial invariance of computer vision systems. They are a...
Latest Articles
What is Dual Homed Topology Definition and Applications
Dual Homed Topology
The main purpose of a dual-homed topology...
What is Point To Multipoint Topology Advantages, Common Uses
What is Point To Multipoint Topology?
A one-to-many communication model...
Types of WAN Topologies: Bus, Ring, Star, Mesh, and Hybrid
What are WAN Topologies
The physical or logical configuration of...
Hub and Spoke Topology Advantages and Disadvantages
In this article, we learn about Hub and Spoke...
What is Point to Point Topology Network, Pros and Cons
What is Point to Point Topology?
A network arrangement known...