There are several forms of Artificial Neural Networks, each with a distinct level of complexity. They all aim to mimic the operation of the human brain in order to tackle complicated issues or tasks. Each form of ANN has a structure similar to neurons and synapses. The difference lies in difficulty, use of applications, and architecture. Differences also exist in how artificial neurons are represented in each vaiety of ANN, as well as in the links among each node. Other distinctions include how data flows via the ANN and node density.
Here are three examples of distinct types of artificial neural networks:
- Feedforward Artificial Neural Networks. As the name implies, data travels in only one way between the input and output nodes. Data travels forward via node levels and does not return through the same layers. Although there are many distinct layers and nodes, the one-way data transit makes Feedforward Neural Networks fairly easy. These kinds of models are mostly utilized for simple categorization issues. Models will outperform typical Machine Learning models, but lack the amount of abstraction seen in deep learning models.
- Convolution Neural Networks (CNN). A CNN features a 3D configuration of neurons rather than the standard 2D array. The first is called the convolutional layer. Layers only analyze information from a limited portion of the visual range. Like a filter, entry features are taken in batches. The network understands pictures in segments and may perform these actions numerous times to finish the entire image processing. The image is converted to greyscale during processing. More pixel value fluctuations will help detect edges, and images can be separated into multiple groups.
- Recurrent Neural Networks (RNN). When a model is built to analyze sequential data, RNNs are effective tools. To best accomplish a goal and enhance predictions, the model will transfer input ahead and cycle it back to earlier phases in the ANN. Recurrent layers are those between the input and output layers where pertinent data is repeated back and stored. The memory of a layer’s output is looped back to the input and held to enhance the workflow for the next input.
The data flow is similar to that of Feedforward Artificial Neural Networks, except each node retains information required to enhance each step. As a result, models can better comprehend the context of input and optimize output prediction.