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Last updated: Feb 7, 2022

Neural Networks

Neural networks are the foundation of deep learning. A network for prediction and accuracy is built by combining various nodes that are arranged in a meaningful structure. Artificial Neural Networks are another name for them. Learn about its different types, applications, and hands-on experience.
Artificial Neural Networks
This blog provides a high-level view of ANNs, particularly their architecture and their uses in the real world.
Bayesian Belief Network in Artificial Intelligence EASY
A Bayesian Belief Network in Artificial Intelligence is a probabilistic graphical model representing dependencies among variables using Bayesian inference principles.
Graph Neural Networks MEDIUM
In this article on Graph Neural Networks (GNN), we will understand GNN's fundamentals, syntax, practical examples with code and output, etc.
Multilayer Perceptron
The objective of this blog is to understand what multilayer perceptrons are.
Loss Functions in Neural Networks
In this article, we will learn about the loss function in neural networks. We’ll see some importance and types of loss functions.
Quantization and Pruning
This blog post will explore two crucial techniques. - Quantization and Pruning. - that enables the development of efficient deep neural networks while maintaining accuracy.
Author Arya27
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Introduction to Hopfield Neural Network
This blog discussed an introduction to Hopfield neural network. We will also discuss its architecture, energy function, and training model.