NLP

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 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 (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 (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 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? A particular kind of Recurrent Neural Network (RNN) architecture called Gated Recurrent Units (GRUs) was created to overcome some of...

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 A basic kind of neural network design, FFNNs are identified by their layered structure and the direction of information...

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 Restricted Boltzmann Machines (RBMs) are unsupervised learning models used in Natural Language Processing (NLP) to learn a probability distribution...