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What is Hierarchical Clustering? Hierarchical clustering is a method of cluster analysis that seeks to build a hierarchy of clusters. It is particularly useful for data that does not naturally fall into distinct groups. Unlike other clustering methods, hierarchical clustering does not require the nu...
What is Principal Component Analysis (PCA)? Principal Component Analysis is a statistical procedure that transforms a set of correlated variables into a set of uncorrelated variables called principal components. The primary goal of PCA is to reduce the dimensionality of a dataset while preserving as...
Understanding Recurrent Neural Networks Recurrent Neural Networks are a class of artificial neural networks designed to recognize patterns in sequences of data. Unlike traditional neural networks, RNNs have connections that form directed cycles, allowing them to maintain a ‘memory’ of pr...
What is Long Short-Term Memory (LSTM)? LSTM is a type of recurrent neural network (RNN) architecture designed to overcome the limitations of traditional RNNs, particularly in handling long-term dependencies. Introduced by Hochreiter and Schmidhuber in 1997, LSTMs are capable of learning order depend...
What is a Transformer? Transformers are a class of neural network architectures introduced in the paper “Attention is All You Need” by Vaswani et al. in 2017. They have since become the backbone of many state-of-the-art models in natural language processing (NLP), such as BERT, GPT, and ...
What is Random Forest? Random Forest is an ensemble learning method primarily used for classification and regression tasks. It operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or mean prediction (regression) of the individual tr...
Understanding Generative Adversarial Networks At its core, a GAN consists of two neural networks: the generator and the discriminator. These networks are set against each other in a game-theoretic scenario, where the generator creates data, and the discriminator evaluates it. The generator aims to p...
What is a Support Vector Machine? Support Vector Machines are supervised learning models used for classification and regression analysis. Developed by Vladimir Vapnik and his colleagues in the 1990s, SVMs are based on the concept of finding a hyperplane that best separates data points into different...
Understanding K-Nearest Neighbors (KNN) K-Nearest Neighbors is a supervised learning algorithm used for classification and regression tasks. It operates on the principle of similarity, where the classification of a data point is determined by the majority class of its ‘k’ nearest neighbo...
What is Naive Bayes? Naive Bayes is a family of probabilistic algorithms based on Bayes’ Theorem, which is used for classification tasks. The term “naive” refers to the assumption that the features in a dataset are independent of each other, which is rarely the case in real-world s...