I
- Identity and Access Management (IAM)
- Incident Response
- Industrial IoT (IIoT)
- Industry 4.0
- Infrastructure as Code (IaC)
- Infrastructure Security
- Innovation Product Design
- Innovation Product Development
- Insider Threat Detection
- Integration Testing
- Intelligent Automation
- Intelligent Process Automation
- Interactive Application Security Testing (IAST)
- Internet of Things (IoT)
- Internet of Things Platform
- Internet of Things Strategy
- Intrusion Detection System (IDS)
- IT/OT Convergence
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