Skip to main content

Neural Network

What are Neural Networks?​

Neural networks are a type of computing model that mimics the networked structure of neurons in the brain. They consist of interconnected processing elements called neurons that work together to produce an output function. Neural networks are made up of input and output layers and often include a hidden layer that transforms input into something the output layer can use.

Neural Network Architectures​

There are several types of neural network architectures, each using different deep learning algorithms. The most common types include:

  • Feed-Forward Neural Network: The most basic type of architecture, where information travels in only one direction from input to output. It includes an input layer, an output layer, and one or more hidden layers. If the hidden layer is more than one, it's called a deep neural network.

  • Recurrent Neural Network (RNNs): A more complex type of network commonly used in speech recognition and natural language processing. RNNs perform the same task for every element of a sequence, with the output depending on previous computations.

  • Convolutional Neural Network (ConvNets or CNNs): A type of network with several layers that filter data into categories. CNNs are highly effective in image recognition, text language processing, and classification. They include an input layer, an output layer, and a hidden layer with multiple convolutional layers, pooling layers, fully connected layers, and normalization layers.

There are many other types of neural networks, such as symmetrically connected networks, Boltzmann machine networks, Hopfield networks, and more. Choosing the right network depends on the data you have to train it with and the specific application you have in mind.

ON THIS PAGE