AI learning refers to the process by which artificial intelligence systems acquire knowledge and improve their performance over time. This typically involves two main approaches: supervised learning (where models are trained on labeled datasets to make predictions or classifications) and unsupervised learning (where patterns and structures are identified in unlabeled data). In addition, reinforcement learning allows AI to learn through trial and error by receiving rewards or penalties for specific actions, fostering decision-making capabilities. The emergence of deep learning in the 2010s, powered by neural networks and large-scale data, has significantly advanced AI learning, enabling breakthroughs in image recognition, natural language processing, and autonomous systems. AI learning relies on iterative training processes, leveraging algorithms such as gradient descent and optimization techniques. Key components include data preprocessing, model building, and evaluation. As AI evolves, ethical considerations around bias, transparency, and fairness in learning models are becoming increasingly important
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Date posted:10-02-2025 16:49:57
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