A neural network is a computational model inspired by the human brain, designed to recognize patterns and relationships within data. It consists of interconnected layers of nodes (or neurons) that process and transmit information to solve tasks like classification, prediction, and optimization. Neural networks are the foundation of many modern artificial intelligence (AI) applications.
Structure and Function:
- Input Layer: Receives raw data, such as text, images, or numerical values.
- Hidden Layers: Perform computations, extracting patterns and features using weighted connections and activation functions.
- Output Layer: Produces the final result, such as a prediction or classification.
- Training Process: Uses algorithms like backpropagation and gradient descent to adjust weights and minimize errors.
Types of Neural Networks:
- Feedforward Neural Networks (FNNs): Data flows in one direction, commonly used for tasks like regression and classification.
- Convolutional Neural Networks (CNNs): Specialized for image and video processing.
- Recurrent Neural Networks (RNNs): Handle sequential data like time series or language.
- Transformers: Used in natural language processing and tasks requiring attention mechanisms.
Why It Matters:
Neural networks are the backbone of deep learning, enabling breakthroughs in AI applications like image recognition, natural language processing, and autonomous systems. Their ability to model complex, nonlinear relationships makes them indispensable for solving problems across industries.
