1. Deep Learning in the Real World

Deep learning models transform examples into useful predictions by passing information through layers of artificial neurons. Instead of following a long list of hand-written rules, a model can discover patterns such as edges in an image, word relationships in text, or signals in medical data.

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A model can recognize a cat, translate a sentence, or generate an image without being given a simple recipe for every case. The surprising part is that many small mathematical adjustments, repeated across layers and examples, can produce abilities that look remarkably flexible.

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Deep learning is already part of tools people use every day: voice assistants, photo search, recommendation systems, navigation, spam filters, and accessibility features. It also helps scientists analyze proteins, weather patterns, satellite images, and other complex data.

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People use deep learning to build vision systems, language tools, scientific models, creative applications, and robots. Future projects can involve training a classifier, adapting a language model, generating images, detecting objects, or testing whether a model is reliable and fair.

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