Deep learning, a subset of machine learning, has revolutionized fields ranging from image recognition to natural language processing. But what exactly is deep learning, and why is it considered a “black box”? This article delves into the core concepts, explores its applications, and addresses the challenges of understanding its inner workings.
What is Deep Learning?
At its heart, deep learning uses artificial neural networks with multiple layers (hence the “deep” in the name) to analyze data. These layers, inspired by the structure of the human brain, learn intricate patterns and representations from vast datasets. Each layer transforms the input data, gradually extracting higher-level features. For example, in image recognition, the first layers might detect edges, the next layers combine edges into shapes, and subsequent layers identify objects.
