Learning of a Neural Network
1. Learning with supervised learning
In supervised learning, the neural network is guided by a teacher who has access to both input-output pairs. The network creates outputs based on inputs without taking into account the surroundings. By comparing these outputs to the teacher-known desired outputs, an error signal is generated. In order to reduce errors, the network’s parameters are changed iteratively and stop when performance is at an acceptable level.
2. Learning with Unsupervised learning
Equivalent output variables are absent in unsupervised learning. Its main goal is to comprehend incoming data’s (X) underlying structure. No instructor is present to offer advice. Modeling data patterns and relationships is the intended outcome instead. Words like regression and classification are related to supervised learning, whereas unsupervised learning is associated with clustering and association.
3. Learning with Reinforcement Learning
Through interaction with the environment and feedback in the form of rewards or penalties, the network gains knowledge. Finding a policy or strategy that optimizes cumulative rewards over time is the goal for the network. This kind is frequently utilized in gaming and decision-making applications.
What is a neural network?
Neural Networks are computational models that mimic the complex functions of the human brain. The neural networks consist of interconnected nodes or neurons that process and learn from data, enabling tasks such as pattern recognition and decision making in machine learning. The article explores more about neural networks, their working, architecture and more.
Table of Content
- Evolution of Neural Networks
- What are Neural Networks?
- How does Neural Networks work?
- Learning of a Neural Network
- Types of Neural Networks
- Simple Implementation of a Neural Network