Learn how layout, activation functions, loss functions, and optimization rules play a role in designing a neural network model.
Join the DZone community and get the full member experience. Artificial neural networks (ANNs) are a commonly used tool in deep learning. In this tutorial, you can learn what they are, learn their basic structure, and code a simple neural network with only one neuron. When you design your own neural networks, there are a number of considerations to take into account. This article will describe a few. Rarely if ever are neural networks as simple as one neuron. For example, the majority of neural networks have at least several layers. You will need hidden layers, layers of artificial neurons between the input and output neuron layers if the data are to be separated in a non-linear fashion. You might want to think about each hidden neuron as a linear classifier. The number of neurons in the first hidden layer should equal the number of lines you would need to draw to classify your data. The later hidden layers and the output layer connect the various linear classifiers. In neural networks, an activation function is a function that determines the artificial neuron’s output, given specific inputs.
