Machine Learning

Adam (Adaptive Moment Estimation) is a popular optimization algorithm used to train machine learning models by iteratively updating parameters based on gradients.

Classification is a supervised machine learning task that assigns input data to one of several predefined categories or classes based on patterns learned from labeled training examples.

Clustering is an unsupervised machine learning technique that automatically groups similar data points together into clusters based on their features, without using any labeled examples.

Gradient descent is an optimization algorithm that finds the minimum of a function by repeatedly moving in the direction of the steepest downward slope. In machine learning it is used to minimize a model's error by adjusting parameters step by step.

A hyperparameter is a value or setting chosen by the user before training a machine learning model that controls the learning process itself.

The learning rate is a hyperparameter that controls the size of the steps an optimization algorithm takes when updating a model's parameters during training.

Logistic Regression is a supervised machine learning algorithm used for binary classification that estimates the probability an input belongs to a particular class.

A loss function quantifies how far a model's predictions are from the true values, serving as the objective that training tries to minimize.

Overfitting happens when a machine learning model learns the training data too closely, including its noise and quirks, so it fails to perform well on new, unseen data.

Regression is a supervised machine learning method that predicts continuous numerical values from input features.

Regularization is a set of techniques in machine learning that reduce overfitting by adding a penalty term to the model's loss function, discouraging overly complex or large parameter values.

Reinforcement Learning (RL) is a machine learning method where an agent learns to make sequential decisions by interacting with an environment, receiving rewards or penalties, and aiming to maximize its long-term reward.

Reinforcement Learning from Human Feedback (RLHF) is a training technique that improves AI models by using human preferences to guide the learning process instead of relying only on fixed rewards.

Self-supervised learning is a machine learning method where a model creates its own training labels directly from the input data, without needing human annotations.

Semi-supervised learning is a machine learning approach that combines a small amount of labeled data with a large amount of unlabeled data to train models more effectively than using either alone.

Supervised learning is a machine learning method where a model is trained on data that already has correct answers attached, so it can learn to predict those answers for new data.

Transfer learning is a machine learning method that reuses a model trained on one task as the starting point for a different but related task.

Underfitting happens when a machine learning model is too simple to capture the patterns in the training data, leading to poor performance on both training and unseen data.

Unsupervised learning is a machine learning method that trains models on unlabeled data to find hidden patterns, structures, or relationships without any guidance on correct outputs.