Deep Learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to analyze various forms of data. Inspired by the human brain's structure, these neural networks aim to replicate its decision-making processes. Traditional machine learning algorithms often plateau in their performance; deep learning models, however, improve as they consume more data. They are especially potent in tasks involving vast amounts of unstructured data, such as image and voice recognition. Deep learning has given rise to innovations like self-driving cars, real-time language translation, and AI-generated art. Key architectures include Convolutional Neural Networks (CNNs) for image tasks and Recurrent Neural Networks (RNNs) for sequential data. While promising, deep learning models can be computationally intensive and require significant data and expertise to train effectively. As technology advances, deep learning continues to be at the forefront of AI research, driving countless modern applications.