A machine learning paradigm is a framework for the design and development of algorithms that learn from data. The paradigm has been used in fields such as computer vision, natural language processing, and speech recognition.
Behaviorism, cognitivism, constructivism, connectivism, and humanism are the most widely acknowledged learning theories today. 1). We shall refer to the named learning paradigms and their associated learning and instructional design theories in this section.
Thus, in both supervised and unsupervised learning, the model (agent) learns based on the training dataset, whereas in RL, the agent learns by interacting with the environment directly. As a result, RL is fundamentally an interaction between the agent and its surroundings.
Supervised learning, unsupervised learning, and reinforcement learning are the three major learning paradigms. Typically, they can be used by any sort of artificial neural network architecture. There are numerous training algorithms for each learning paradigm.
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