S
- Sandbox Environments
- Secure Coding Practices
- Security Automation
- Security Awareness Training
- Security Champions
- Security Information and Event Management (SIEM)
- Security Orchestration
- Security Posture
- Shift-Left Security
- Smart City
- Smart Home
- Smart Manufacturing
- Smart Meters
- Smart Products
- Smart Spaces
- Software as a Service (SaaS)
- Software Composition Analysis (SCA)
- Software Defined Networking (SDN)
- Software Development Life Cycle (SDLC)
- Static Application Security Testing (SAST)
- Structured Data
Neural Network
Simple Definition for Beginners: A neural network is a type of computer system designed to simulate the way the human brain processes information, helping machines to recognize patterns and make decisions.
Common Use Example: An image recognition system uses a neural network to identify objects in photos, such as distinguishing between cats and dogs.
Technical Definition for Professionals: A neural network is a computational model inspired by the structure and function of the human brain. It consists of interconnected layers of nodes (neurons), including an input layer, one or more hidden layers, and an output layer. Each connection between nodes has an associated weight that adjusts as the network learns from data. Neural networks are used in machine learning for tasks such as classification, regression, and clustering. Advanced forms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are specifically designed for image processing and sequential data analysis, respectively. Training a neural network involves forward propagation, where input data passes through the layers to produce an output, and backpropagation, where errors are calculated and weights are adjusted to improve accuracy.
Neural Network