Deep learning is a subfield of machine learning that mimics the way the human brain works through artificial neural networks (ANNs) that consist of many layers. Each layer processes information in increasingly abstract ways, starting with raw input data and continuing through multiple layers of interconnected nodes (neurons). These nodes apply mathematical operations to the input data, transforming it into representations that capture hierarchical patterns and features. When training a deep learning model, it is fed labeled data (supervised learning) or it can learn patterns from unlabeled data (unsupervised learning). During training, the model iteratively adjusts the weights (parameters) of the connections between neurons using algorithms such as backpropagation and stochastic gradient descent, minimizing the difference between predicted outputs and actual targets (loss function). This optimization process refines the model's ability to generalize from training data to unknown examples, thereby improving its prediction accuracy. Deep learning is well suited for tasks such as image and speech recognition, natural language processing, and autonomous driving, where it can automatically learn complex patterns and make decisions without explicit programming. The success of deep learning depends on its ability to efficiently process large amounts of data and leverage powerful compute resources such as GPUs or TPUs (tensor processing units) to accelerate training and inference tasks.

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