What is a neural network?
A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics how the human brain operates. Neural networks refer to neurons, either artificial or organic. They can adapt to changing input; thus, the network generates the best possible result without redesigning the output criteria.
What are the types of neural networks?
- Feedforward Neural Network – Artificial Neuron: This is the simplest type of artificial neural network. Data is passed through various input nodes until it finally reaches the output node.
- Radial Basis Function Neural Network: This type of neural network considers the distance of any certain point relative to the center. These networks have two layers. The features are paired up with the radial basis function in the inner layer. The output of the given features is considered when the same output gets calculated in the next step.
- Multilayer Perceptron: This neural network has three or more layers. It is basically used to classify the data that cannot be linearly separated. This type of artificial neural network is fully connected because each node present in a layer is connected to nodes in the next layer.
- Convolutional Neural Network: This type of neural network uses a variation of the multilayer perceptrons. They contain one or more layers that can be pooled or entirely interconnected.
- Recurrent Neural Network – Long Short Term Memory: In this type of artificial neural network, a particular layer’s output is saved and then fed back to the input. It helps in predicting the outcome of a layer. The formation of the first layers is the same as it is in the feedforward network.
- Modular Neural Network: This neural network has many different networks functioning independently, performing sub-tasks. They don’t interact with one another during the computation process. They independently work to achieve the output.
- Sequence-to-Sequence Models: This neural network contains two recurrent neural networks. An encoder is present that a decoder process processes the input and the output. The encoder and decoder can use similar or even different parameters.
What are the benefits of a neural network?
The benefits of a neural network are as follows:
- Store information on the entire network
- Ability to work with insufficient knowledge
- Good fault tolerance
- Distributed memory
- Gradual corruption
- Ability to train machine
- Ability of parallel processing