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Deep learning is a subfield of artificial intelligence (AI) and machine learning (ML) that focuses on the development and application of neural networks, which are computational models inspired by the structure and function of the human brain. It is particularly well-suited for tasks involving large amounts of data, such as
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Reinforcement Learning (RL) is a type of machine learning within the field of artificial intelligence (AI) that focuses on training agents to make a sequence of decisions in an environment in order to achieve a specific goal. It is a learning paradigm inspired by behavioral psychology, where agents learn by
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Semi-supervised machine learning is a type of machine learning paradigm that falls between supervised learning and unsupervised learning. In semi-supervised learning, the training dataset contains a combination of labeled and unlabeled data. This approach is particularly useful when obtaining a large amount of labeled data is costly or time-consuming, but
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Unsupervised machine learning is a category of machine learning where the algorithm is trained on unlabeled data, meaning that it's given input data without any corresponding output or target variable. Unlike supervised learning, where the algorithm learns to make predictions based on labeled data (input-output pairs), unsupervised learning
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Supervised machine learning in Artificial Intelligence (AI) and Machine Learning (ML) is a type of machine learning where an algorithm learns from a labeled dataset, which means that each input in the dataset is paired with the corresponding desired output. The goal of supervised learning is to learn a mapping