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Logistic Regression
Logistic regression is a statistical model used to analyze the relationship between a binary dependent variable and one or more in...
Regularization Techniques
Learn about the different regularization techniques used in machine learning to prevent overfitting and improve model performance.
Classification Algorithms
Learn about different types of classification algorithms used in machine learning, including decision trees, SVM, naive Bayes, and...
Decision Boundary
Discover the concept of decision boundary in machine learning and how it separates different classes in a dataset. Understand its ...
Naive Bayes Classifier
Naive Bayes Classifier is a simple yet powerful algorithm used for classification tasks in data science, machine learning, and nat...
L1 Regularization (Lasso)
Learn about L1 Regularization (Lasso) technique used in machine learning to prevent overfitting by adding penalty to the absolute ...
L2 Regularization (Ridge)
Learn about L2 regularization (Ridge), a technique used in machine learning to prevent overfitting by adding a penalty term to the...
Support Vector Machines (SVM)
A powerful machine learning algorithm, Support Vector Machines (SVM) is used for classification and regression tasks, offering hig...
Elastic Net Regularization
Elastic Net Regularization is a technique that combines Lasso and Ridge regularization to improve model performance and handle mul...
k-Nearest Neighbors (k-NN)
Learn about k-Nearest Neighbors (k-NN) algorithm, a simple yet powerful classification method in machine learning. Understand its ...
Dropout Regularization
Learn how dropout regularization technique helps prevent overfitting in neural networks by randomly deactivating certain neurons d...
Neural Network Architectures
Explore various neural network architectures such as CNNs, RNNs, and Transformers for deep learning applications. Understand their...
Feedforward Neural Networks
A concise overview of feedforward neural networks, their structure, and functionality in artificial intelligence applications.
Multilayer Perceptrons (MLPs)
Discover how multilayer perceptrons (MLPs) work in neural networks to solve complex problems with multiple layers of interconnecte...
Activation Functions
Learn about Activation Functions - essential components of neural networks that introduce non-linearity, enabling complex relation...
Sigmoid Function
The Sigmoid Function is a mathematical function that maps any real value to a value between 0 and 1. It is commonly used in machin...
Tanh Function
The tanh function is a mathematical function that maps real numbers to the range (-1,1). Learn more about its properties and appli...
Rectified Linear Unit (ReLU)
Learn about Rectified Linear Unit (ReLU), a popular activation function in neural networks that helps prevent the vanishing gradie...
Leaky ReLU
Leaky ReLU is a type of activation function used in neural networks, allowing a small gradient when the input is negative to preve...
Exponential Linear Unit (ELU)
Learn about Exponential Linear Unit (ELU) activation function in neural networks. Understand its benefits and how it can improve m...
Swish Activation Function
Meta Description: Learn about Swish activation function, a popular alternative to ReLU, for faster convergence and improved perfor...
Transfer Learning Techniques
Learn about transfer learning techniques and how they can help you leverage pre-trained models to improve the performance of your ...
Softmax Function
Discover the mathematical formula behind the Softmax function, a popular choice for classification problems in machine learning.
Fine-Tuning
Learn about the process of fine-tuning, where small adjustments are made to improve performance or efficiency in various systems a...
Loss Functions
Learn about loss functions in machine learning and understand how they are used to measure the difference between predicted and ac...