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Deep learning uses multi-layered (deep) neural networks to extract high-level features from raw data. It is the underlying technology that powers modern AI breakthroughs, including computer vision, speech recognition, and large language models. The "deep" refers to the numerous layers of artificial neurons that process data sequentially.
A subset of machine learning based on artificial neural networks with multiple layers.
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In traditional machine learning, human engineers manually extract and define the features the algorithm should look for. Deep learning algorithms perform feature extraction automatically. When fed raw pixel data, the first layers might detect edges, middle layers detect shapes, and final layers combine them to recognize faces.
They are loosely inspired by the biological brain, using interconnected nodes (neurons) to pass signals. However, the exact mathematical optimization algorithms (like backpropagation) are quite different from biological learning processes.
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