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Machine learning is broadly categorized by how learning signals are provided to the model. In supervised learning, models are trained on labeled data—each example pairs an input wi...
Many foundational algorithms form the backbone of practical machine learning. Linear regression models the relationship between input features and a continuous target by fitting a...
Training a machine learning model requires defining an objective and a procedure to minimize it. The loss function quantifies how far predictions are from the true targets; common...
The quality and form of input features often matter more than algorithm choice. Feature engineering creates, transforms, or selects features to expose useful signal to the model, i...
Neural networks are computational models inspired by biological neurons. The simplest unit is the perceptron, which computes a weighted sum of inputs, adds a bias, and applies a st...
Different data modalities call for specialized architectures. Convolutional Neural Networks (CNNs) are designed for spatial data such as images and audio spectrograms. A convolutio...
Natural language processing builds on several foundational techniques. Tokenization splits raw text into smaller units called tokens—words, subwords, or characters. Modern systems...
Deploying machine learning at scale requires careful systems engineering. Retrieval-Augmented Generation (RAG) augments a language model by retrieving relevant external documents a...