Deep learning represents a significant leap in the field of artificial intelligence, enabling machines to learn and make decisions with unprecedented accuracy. Unlike traditional machine learning algorithms, which require manual feature extraction, deep learning models automatically learn to identify intricate patterns and representations from vast amounts of data. This capability has led to groundbreaking advancements in numerous applications, from self-driving cars to real-time language translation.
The roots of deep learning can be traced back to the development of artificial neural networks, inspired by the biological neural networks in the human brain. These artificial networks consist of interconnected layers of neurons, each performing a linear transformation followed by a non-linear activation. The depth of these networks, characterized by the number of layers, allows for the hierarchical learning of features, where each successive layer captures more abstract representations of the input data.