Neural Network - A computational model inspired by the human brain, consisting of layers of nodes (neurons).
Neurosymbolic AI – A combination of neural networks and symbolic reasoning for AI with logical reasoning capabilities.
Overfitting – When an ML model learns noise instead of patterns, performing well on training data but poorly on new data.
Quantum AI – AI techniques that leverage quantum computing for complex problem-solving.
Recurrent Neural Network (RNN) – A neural network designed for sequential data, such as time series and speech.
Reinforcement Learning (RL) - A type of ML where agents learn by taking actions in an environment to maximize rewards.
Self-Supervised Learning – An ML approach where the model learns from data without explicit labels.
Semi-Supervised Learning - A hybrid ML approach using both labeled and unlabeled data.
Singularity – A hypothetical point where AI surpasses human intelligence.
Supervised Learning - A type of ML where models learn from labeled training data.