Deep learning neural networks (DLNN) are a subset of machine learning techniques that model high-level abstractions in data through multiple layers of interconnected nodes. These networks can automatically learn representations from raw data, enabling them to perform tasks such as image and speech recognition, natural language processing, and game playing. This section provides an overview of DLNN, discusses its role in automated test case generation and optimization, and highlights successful applications and future trends in this area.
Structure of Deep Learning Neural Networks
DLNN consists of interconnected layers of artificial neurons, also known as nodes. These layers can be grouped into three categories: