Supervised Learning

Few-Shot Learning

Few-shot learning is a machine learning approach in which a model learns to perform a new task from only a small number of labeled examples, rather than a larg…

Zero-Shot Learning

Zero-shot learning is a machine learning capability where a model performs a task it was never explicitly trained or shown examples for, relying only on its ge…

Reinforcement Learning

Reinforcement learning (RL) is a machine learning paradigm in which an agent learns to make decisions by taking actions in an environment and receiving rewards…

Supervised Learning

Supervised learning is a machine learning approach in which a model learns to map inputs to outputs by training on a dataset of labeled examples.

Unsupervised Learning

Unsupervised learning is a machine learning approach in which a model finds patterns, structure, or groupings in data without being given labeled outputs.

Self-Supervised Learning

Self-supervised learning is a machine learning technique in which a model generates its own supervisory labels from unlabeled data, learning by predicting hidd…

Overfitting

Overfitting occurs when a machine learning model learns the training data too closely, including its noise and idiosyncrasies, resulting in poor performance on…

Underfitting

Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both…

Gradient Descent

Gradient descent is an optimization algorithm that iteratively adjusts a model's parameters in the direction that most reduces its error, or loss, in order to…

Backpropagation

Backpropagation is the algorithm used to train neural networks by efficiently computing how much each parameter contributed to the model's error, so those para…