官术网_书友最值得收藏!

Perceptron

To start, we will introduce the perceptron model. The perceptron is the simplest neural network model. It can learn a linear mapping based on the input and output when trained on a labeled training dataset. A linear mapping is the sum of a product of weights on a set of input variables, otherwise known as features. The final sum is passed through a step function to select one of the binary values in the case of a classification problem. The following diagram represents a perceptron:

The weights are derived from the training data by a process called learning. The process of learning will be explained later in this chapter. The perceptron uses the unit step function for the output prediction. The final activation output can be 0 or 1, corresponding to the binary class in the training data. While the unit step function is the simplest activation function, we will touch upon other types of activation functions that are widely used in modern deep learning architectures in the following section.

主站蜘蛛池模板: 乌海市| 招远市| 遂溪县| 岫岩| 临泉县| 呼伦贝尔市| 炉霍县| 玛沁县| 昌平区| 扎赉特旗| 宁国市| 霍邱县| 长治县| 寿阳县| 张家口市| 石景山区| 中山市| 昌乐县| 于田县| 徐闻县| 铅山县| 武安市| 武汉市| 关岭| 渝北区| 吉林省| 定安县| 延庆县| 盘锦市| 张掖市| 望都县| 万安县| 桑日县| 镶黄旗| 晋中市| 汉阴县| 武强县| 新昌县| 应城市| 安庆市| 桦川县|