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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 LightGBM? LightGBM is a gradient boosting framework that uses tree-based learning algorithms. It is designed to be distributed and efficient, making it ideal for large-scale data processing. Unlike traditional gradient boosting methods, LightGBM grows trees leaf-wise rather than level-wise, ...
What is CatBoost? CatBoost, short for Categorical Boosting, is an open-source machine learning library that is designed to handle categorical data efficiently. Unlike other gradient boosting libraries, CatBoost automatically deals with categorical features, eliminating the need for extensive preproc...
Understanding K-Means Clustering K-Means clustering is an unsupervised machine learning algorithm used to partition a dataset into distinct groups, or clusters. The primary goal is to minimize the variance within each cluster while maximizing the variance between clusters. This is achieved by iterat...
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...