Artificial Neural Network
Artificial Neural Network (ANN) is a computational model inspired by the structure and function of biological neural networks. It is a type of machine learning algorithm that has gained popularity in recent years due to its ability to solve complex problems such as image recognition, natural language processing, and speech recognition.
How does ANN work?
ANN consists of multiple layers of interconnected nodes, also known as artificial neurons. Each neuron receives input from one or more neurons and computes a weighted sum of those inputs. The sum is then passed through an activation function, which determines whether the neuron will fire or not. The output of a neuron is then fed as input to another neuron in the next layer until the final output is obtained.
Diagram of Artificial Neural Network
The process of training an ANN involves adjusting the weights of the neurons to minimize the error between the predicted output and the actual output. This is done using a process called backpropagation, which calculates the gradient of the error with respect to the weights and updates them accordingly.
Types of ANN
There are several types of ANN, each with its own unique structure and function. Here are a few examples:
Feedforward Neural Network
Feedforward Neural Network is the simplest and most common type of ANN. It consists of an input layer, one or more hidden layers, and an output layer. The input layer receives the input data, and the output layer produces the output. The hidden layers perform the computations between the input and output layers.
Recurrent Neural Network
Recurrent Neural Network is a type of ANN that is capable of processing sequential data. It has feedback connections that allow information to flow in both directions, making it suitable for tasks such as speech recognition and natural language processing.
Convolutional Neural Network
Convolutional Neural Network is a type of ANN that is specifically designed for image recognition. It consists of convolutional layers that perform a convolution operation on the input data, pooling layers that reduce the size of the output, and fully connected layers that produce the final output.
Applications of ANN
ANN has a wide range of applications in various fields such as finance, healthcare, transportation, and entertainment. Some examples include:
- Fraud detection in banking and finance
- Medical diagnosis in healthcare
- Self-driving cars in transportation
- Recommendation systems in e-commerce
- Image and speech recognition in entertainment
Conclusion
Artificial Neural Network is a powerful machine learning algorithm that has revolutionized the field of artificial intelligence. Its ability to learn from data and solve complex problems has made it an essential tool in various industries. As technology continues to advance, it is likely that ANN will continue to play a significant role in shaping our future.