Disadvantages of Neural Networks
Neural networks, while powerful, are not without drawbacks and difficulties:
- Computational Intensity: Large neural network training can be a laborious and computationally demanding process that demands a lot of computing power.
- Black box Nature: As “black box” models, neural networks pose a problem in important applications since it is difficult to understand how they make decisions.
- Overfitting: Overfitting is a phenomenon in which neural networks commit training material to memory rather than identifying patterns in the data. Although regularization approaches help to alleviate this, the problem still exists.
- Need for Large datasets: For efficient training, neural networks frequently need sizable, labeled datasets; otherwise, their performance may suffer from incomplete or skewed data.
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