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: 交口县| 石门县| 镇安县| 花垣县| 洱源县| 榆林市| 从江县| 上林县| 梅河口市| 盐城市| 鄂托克前旗| 武冈市| 金山区| 若羌县| 江安县| 南通市| 喜德县| 桐乡市| 垫江县| 苗栗县| 珲春市| 雷山县| 新晃| 永泰县| 南城县| 浪卡子县| 长汀县| 大关县| 民丰县| 乌拉特后旗| 青铜峡市| 怀仁县| 肥东县| 上杭县| 高安市| 汨罗市| 会同县| 阳新县| 松溪县| 呼玛县| 称多县|