When neural networks become deep—meaning they contain many hidden layers—they are referred to as deep neural networks (DNNs). Each layer of a deep network learns progressively more abstract representations of the input data.
- Hierarchical Learning: In deep learning, each layer in the network captures features at different levels of abstraction. For instance, in an image recognition task, early layers may detect edges or simple shapes, while deeper layers recognize more complex patterns like objects or faces.
- Automatic Feature Learning: One of the key advantages of deep learning is that the network can automatically learn and optimize the best features for a specific task, without requiring explicit human intervention.
- Scalability: Deep learning models scale extremely well with large datasets and computational resources, allowing them to outperform traditional methods on tasks that involve massive data like image classification, language translation, and speech recognition.