Difference between Neural Network and Artificial Neural network

A Neural Network is a machine learning model that attempts to simulate the behavior of the human brain. Artificial Neural Networks (ANNs) are a type of Neural Network specifically designed to replicate the function of a biological neuron. 

The key difference between Neural Networks and Artificial Neural Networks is that Artificial Neural Networks are specifically designed to replicate the function of a biological neuron while traditional Neural Networks use a more simplified model to accomplish the same goal. Traditional Neural Networks are much easier to train, as they rely on a single neuron for activation and are capable of faster learning. Artificial Neural Networks are more complicated, requiring multiple layers of neurons that all work together in a complex network.

Additionally, Artificial Neural Networks are more adaptive, allowing them to change based on incoming information or data. This gives them the ability to solve more complex problems. On the other hand, traditional Neural Networks rely on fixed structures and are better at recognizing patterns or features in existing data. 

In summary, the key difference between Artificial Neural Networks and Neural Networks is the complexity of the design. Artificial Neural Networks require multiple layers of neurons and are more adaptive, allowing them to solve more complex problems. Traditional Neural Networks are much easier to train, but rely on fixed structures and are better at recognizing patterns.