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Graph Convolutional Networks (GCNs): Architectural Insights and Applications

Last Updated: 21 June 2024

Graph Convolutional Networks (GCNs) have emerged as a powerful class of deep learning models designed to handle graph-structured data. Unlike traditional Convolutional Neu...read more

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Data Science Blogathon 2024

Least Mean-Squares Algorithm in Neural Networks

Last Updated: 15 July 2024

The Least Mean-Squares (LMS) algorithm is a widely used adaptive filter technique in neural networks, signal processing, and control systems. Developed by Bernard Widrow a...read more

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AI-ML-DS With Python

Data Science Blogathon 2024

Linear Regression vs. Neural Networks: Understanding Key Differences

Last Updated: 14 June 2024

Linear Regression and Neural Networks are two fundamental techniques in the machine learning toolkit. Linear Regression is a simple, yet powerful, statistical method for m...read more

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Machine Learning

Data Science Blogathon 2024

Feedback System in Neural Networks

Last Updated: 02 July 2024

A feedback system in neural networks is a mechanism where the output is fed back into the network to influence subsequent outputs, often used to enhance learning and stabi...read more

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AI-ML-DS With Python

Unconstrained Optimization Techniques in Neural Networks

Last Updated: 04 July 2024

Unconstrained optimization plays a crucial role in the training of neural networks. Unlike constrained optimization, where the solution must satisfy certain constraints, u...read more

Machine Learning

optimization-technique

Machine Learning

Feedback Neural Networks: Structure, Training, and Applications

Last Updated: 26 June 2024

Neural networks, a cornerstone of deep learning, are designed to simulate the human brain's behavior in processing data and making decisions. Among the various types of ne...read more

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Data Science Blogathon 2024

Differences Between Bayesian Networks and Neural Networks

Last Updated: 26 June 2024

Bayesian networks and neural networks are two distinct types of graphical models used in machine learning and artificial intelligence. While both models are designed to ha...read more

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Perceptron Convergence Theorem in Neural Networks

Last Updated: 15 July 2024

The Perceptron Convergence Theorem is a fundamental concept in machine learning, showing how a simple algorithm, the perceptron, can learn to classify items accurately. It...read more

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AI-ML-DS With Python

Data Science Blogathon 2024

HyperParameter Tuning: Fixing Overfitting in Neural Networks

Last Updated: 16 July 2024

Overfitting is a pervasive problem in neural networks, where the model becomes too specialized to the training data and fails to generalize well to new, unseen data. This ...read more

Machine Learning

AI-ML-DS With Python

Data Science Blogathon 2024

Repeated Training on the Same Data: Implications for Model Performance

Last Updated: 21 April 2025

In machine learning, the process of training a model is crucial for its performance on unseen data. However, a common question arises: What happens if I fit my model on th...read more

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Machine Learning

AI-ML-DS With Python

Data Science Blogathon 2024

Batch Size in Neural Network

Last Updated: 06 January 2025

Batch size is a hyperparameter that determines the number of training records used in one forward and backward pass of the neural network. In this article, we will explore...read more

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RNN vs LSTM vs GRU vs Transformers

Last Updated: 29 May 2025

When working with data that comes in a sequence like sentences, speech or time-based information we need special models that can understand the order and connection betwee...read more

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Are Neural Networks Supervised or Unsupervised?

Last Updated: 07 January 2025

Neural networks can be both supervised and unsupervised depending on how they are trained and the task they are designed to perform. In supervised learning, they rely on l...read more

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Why ReLU function is not differentiable at x=0?

Last Updated: 06 January 2025

ReLU activation function introduces non-linearity to the neural networks, enabling them to capture complex patterns in the data. It is defined as:\text{ReLU}(x) = \max(0, ...read more

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Defensive distillation

Last Updated: 05 June 2025

Defensive distillation is a technique aimed at improving the robustness of deep neural networks against adversarial attacks. It involves training a more robust network by ...read more

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AI-ML-DS With Python

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