As all journeys start with a single step, all neural network models can be formed by defining a single neuron. Therefore, we will introduce the single neuron in this chapter. To do so, we will start with its structure and mathematical definition. Then, we will consider forming the single neuron in TensorFlow and Keras. Although we will benefit from the latter option in the following sections, forming the neuron in TensorFlow gives valuable insight on working principles of neural networks. Therefore, we will cover it in this chapter. Afterward, we will focus on training the single neuron. Here, we will explain the mechanism in training, loss function, and optimizers used in training. We will cover training both in TensorFlow and Keras. Next, we will form a classifier and regressor by the single neuron. Finally, we will consider real-life applications introduced in the previous chapters now from the single neuron perspective.