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Using Latent Factor Models To Uncover Hidden Patterns
Latent Factor Models
A family of methods known as latent factor models aims to identify the underlying, invisible (latent) components that underlie the associations seen...
Contextual Embeddings: The Word Representations In NLP
Contextual Embeddings in NLP
Advanced vector representations of words called contextual embeddings are made especially to capture a word's meaning in the context in which...
SVD NLP: Singular Value Decomposition in NLP
SVD NLP
Singular Value Decomposition (SVD) is a potent algebraic method mostly used for matrix factorization and dimensionality reduction in Natural Language Processing (NLP). The...
What Is A Radial Basis Function Networks With Example?
What is a Radial Basis Function (RBF) Networks
In contrast to feed-forward neural networks, radial basis function (RBF) networks are essentially a different design.
RBF networks...
Entity Linking in NLP with Wikipedia, DBpedia, and Freebase
Entity Linking in NLP
What is Entity Linking?
One task in natural language processing is entity linking, which entails connecting a textual mention to an ontology's...
What is Gated Recurrent Unit? What is the purpose of the GRU
What is Gated Recurrent Unit?
A particular kind of Recurrent Neural Network (RNN) architecture called Gated Recurrent Units (GRUs) was created to overcome some of...
Applications of BERT, Training Process, And Architecture
We will go over BERT's definition, architecture, training procedure, applications of BERT, comparison to GPT, and more in this blog.
BERT stands for Bidirectional Encoder...
Feed Forward Neural Network Definition And Architecture
Feed Forward Neural Network Definition
A basic kind of neural network design, FFNNs are identified by their layered structure and the direction of information...
Neural Turing Machines Architecture, Advantages And Examples
Neural Turing Machines (NTMs)
A class of neural network models called Neural Turing Machines (NTMs) was created to overcome the drawbacks of conventional recurrent neural...
An Introduction To Restricted Boltzmann Machines Explained
An introduction to Restricted Boltzmann Machines
Restricted Boltzmann Machines (RBMs) are unsupervised learning models used in Natural Language Processing (NLP) to learn a probability distribution...
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