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.

Author Arun Nawani

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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.

Author Kanak Rana

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Multilayer Perceptron

The objective of this blog is to understand what multilayer perceptrons are.

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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.

Author Kanak Rana

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What is Neural Network EASY

In this article, we will explore what is Neural Network, how they work, and the different types of neural networks. We will also see a simple implementation of a neural network using Python.

Author Riya Singh

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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.

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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.

Author Ayushi Goyal

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Feedforward Neural Networks

A feedforward neural network is one that does not have a cycle. Learn about its concept, implementation, and backpropagation.

Introduction to Feedforward Neural Networks MEDIUM

In this blog, we discussed Feedforward neural networks, and it’s architecture, developing a simple model and applications.

Author Mandla Dharani

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Representation Power of Neural Networks

The blog discusses what is meant by the representation power of FNN and why we need deep neural networks representing complex functions, along with a simplified example.

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Backpropagation in Neural Networks EASY

Discover backpropagation in neural networks, including its algorithm, model components, benefits, and drawbacks. Explore various types and essential derivatives.

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Challenges in Neural Network Optimization in Deep Learning HARD

Discover key challenges in neural network optimization in deep learning, from vanishing gradients to overfitting, and learn strategies to overcome them.

Author Sanjana kumari

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Difference Between Backpropagation and Stochastic Gradient Descent

In this article, we will learn about backpropagation and stochastic gradient descent. Difference between backpropagation and gradient descent.

Author soham Medewar

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Optimization of Neural Network

Learn about different algorithms, architectures, and techniques for optimising neural networks for better prediction and analysis, as well as improving the accuracy of any model.

Gradient Descent with Momentum

This article will focus on the limitations of gradient descent and how momentum overcomes those shortcomings. Finally, we will look into the maths part.

Author Mayank Goyal

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Nesterov Accelerated Gradient EASY

Nesterov Accelerated Gradient (NAG) is an optimization technique that improves gradient descent by adding a momentum term for faster convergence and reduced oscillations.

Author Mohammad Yawar

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What is AdaGrad?

This blog is about AdaGrad. Why it is used, and what is the concept behind it.

Author Mohammad Yawar

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In this blog, we will review the Gradient Descent algorithm and its drawbacks and the various Optimizers to remove the drawback.

Author Rajkeshav

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This article focuses on the RMSProp optimizer, the reason behind using it, the work and mathematics behind it. Finally at last, we will look into a python implementation.

Author Mayank Goyal

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Adaptive Moment Estimation (ADAM)

In this article, we will learn ADAM (adaptive moment estimation) optimizer. The derivation of ADAM optimizer and implementation of it.

Author soham Medewar

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Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm

In this article, we will discuss the L-BFGS(Limited-Memory Broyden-Fletcher-Goldfarb-Shanno) Algorithm and its maths.

Author soham Medewar

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Eigenvalues and Eigenvector

Linear algebra is used in machine learning and deep learning. Learn about Eigenvalues and Eigenvectors, which can aid in the reduction of equations and matrices when working on models.

Eigenvalues and Eigenvectors

In this blog post, we’ll learn about the theory behind eigenvectors and eigenvalues and their implementation in Python.

Author Shabeg Singh Gill

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Eigenvalue Decomposition EASY

In this blog, We'll go over the definition of eigen decomposition and the notions that go along with it.

Author Priyanshu Sharma

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Singular Value Decomposition

In this article, we will discuss Singular Value Decomposition(SVD). Implementation and use of Singular Value Decomposition.

Author soham Medewar

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Singular Value Decomposition and Eigenvalue Decomposition EASY

This article aims to understand the difference between Singular Value Decomposition and Eigenvalue Decomposition

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