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Types of artificial neural networks
Types of neural networks (NN) include a family of techniques. The simplest types have static components, including number of units, number of layers, unit weights and topology. Dynamic NNs evolve via learning. Some types allow/require learning to be "supervised" by the operator, while others operate independently. Some types operate purely in hardware, while others are purely software and run on general purpose computers. While there are dozens of different types of neural network architecture, the four most commonly used architectures are as follows: Feedforward neural networks (FNN): Also called a Multi-Layer Perceptron (MLP), these are basic neural networks with an unidirectional information flow. They lack memory and any sense of spatial or temporal data; however their simple architecture is useful for basic pattern recognition. Convolutional neural networks (CNN): Uses convolutional filters (small sliding windows) to scan across input data, detecting local features like edges, textures, or shapes. They are typically used to analyze visual and other two-dimensional data. They are spatially aware, but lack in sequential or variable-length dependencies. Recurrent neural networks (RNN): Contains loops that allow information to persist. The network has a "hidden state" that acts as memory, updated at each time step. RNNs and their variants (e.g. LSTM or Gated recurrent unit) naturally handles sequential data of variable length (time-series, speech, text). Transformers: These use attention to analyze every token in the input stream against every other token in the stream. Transformers were developed by Google engineers in their landmark paper titled "Attention Is All You Need" and they form the main backbone of modern chatbots and Large Language Model applications such as ChatGPT and DeepSeek.