ZeroDivisionError in loss function calculation
A ZeroDivisionError occurs when Python tries to divide a number by zero, which is mathematically undefined. In machine learning, this error can arise during the calculation of loss functions, such….
Tag: Gradient Descent
Not normalizing data before training ML models
Data normalization is a crucial preprocessing step in machine learning. If skipped, it can negatively affect model performance, particularly for algorithms that rely on feature scaling. In this guide, I….
Long Short-Term Memory Networks (LSTMs)
Long Short-Term Memory Networks (LSTMs): Detailed Explanation Long Short-Term Memory Networks (LSTMs) are a specialized type of Recurrent Neural Networks (RNNs) that are specifically designed to overcome the limitations of….
Convolutional Neural Networks (CNNs)
Convolutional Neural Networks (CNNs): Detailed Explanation Convolutional Neural Networks (CNNs) are a class of deep neural networks specifically designed to work with grid-like data, such as images, audio, and video…..
Activation Functions (ReLU, Sigmoid, Tanh)
Activation Functions in Neural Networks: Detailed Explanation Activation functions play a crucial role in neural networks, enabling them to capture non-linear relationships in the data. Without activation functions, a neural….
Forward and Backpropagation
Forward and Backpropagation in Neural Networks: Detailed Explanation Forward and backward propagation are two key steps in the training process of a neural network. These steps are fundamental to how….
Introduction to Neural Networks
Introduction to Neural Networks: Detailed Explanation A Neural Network (NN) is a computational model inspired by the way biological neural networks in the human brain process information. It is a….
t-SNE for High-Dimensional Data
t-SNE (t-Distributed Stochastic Neighbor Embedding) is a powerful technique for dimensionality reduction and visualization of high-dimensional data. Unlike PCA, which is a linear method, t-SNE is a non-linear technique that….
Anomaly Detection
Anomaly Detection – A Comprehensive Guide 1. Introduction to Anomaly Detection Anomaly Detection is the process of identifying rare or unusual patterns in data that do not conform to expected….
Hierarchical Clustering
Hierarchical Clustering: A Comprehensive Guide 1. Introduction to Hierarchical Clustering Hierarchical Clustering is an unsupervised machine learning algorithm used to group similar objects into clusters. Unlike K-Means, it does not….