What are Neural Network?
Neural Networks are brain inspired machine learning models. The basic structure of neural network includes layers and activation functions.
- Neural networks are built up of interconnected nodes called artificial neurons. These are loosely based on biological neurons in our brains.
- These artificial neurons are organized in layers, typically consisting of an input layer, one or more hidden layers, and an output layer.
The neural network adjust the weights of the connections between neurons to minimize the difference between predicted output and actual target values during training.
Advantages of Neural Network
- Neural networks are able to handle structured as well as unstructured data.
- Capable of learning and extracting relevant features from the raw data.
Limitations of Neural Network
- Complex model are difficult to understand and the black box layer hinder the transparency of the model’s predictions.
- Computationally expensive.
Polynomial Regression vs Neural Network
In this article, we are going to compare polynomial regression and neural networks.