官术网_书友最值得收藏!

Defining feedforward networks

Deep feedforward networks, also called feedforward neural networks, are sometimes also referred to as Multilayer Perceptrons (MLPs). The goal of a feedforward network is to approximate the function of f?. For example, for a classi?er, y=f?(x) maps an input x to a label y. A feedforward network defines a mapping from input to label y=f(x;θ). It learns the value of the parameter θ that results in the best function approximation.

We discuss RNNs in Chapter 5Recurrent Neural Networks. Feedforward networks are a conceptual stepping stone on the path to recurrent networks, which power many natural language applications. Feedforward neural networks are called networks because they compose together many di?erent functions which represent them. These functions are composed in a directed acyclic graph.

The model is associated with a directed acyclic graph describing how the functions are composed together. For example, there are three functions f(1), f(2), and f(3) connected to form f(x) =f(3)(f(2)(f(1)(x))). These chain structures are the most commonly used structures of neural networks. In this case, f(1) is called the first layer of the network, f(2) is called the second layer, and so on. The overall length of the chain gives the depth of the model. It is from this terminology that the name deep learning arises. The final layer of a feedforward network is called the output layer.

Diagram showing various functions activated on input x to form a neural network

These networks are called neural because they are inspired by neuroscience. Each hidden layer is a vector. The dimensionality of these hidden layers determines the width of the model.

主站蜘蛛池模板: 博白县| 南岸区| 许昌县| 吉安县| 通州市| 滨州市| 闻喜县| 依兰县| 乌鲁木齐县| 河津市| 招远市| 台北市| 佛山市| 清涧县| 广宗县| 东莞市| 萨嘎县| 临猗县| 萝北县| 萍乡市| 拜泉县| 昌平区| 时尚| 额尔古纳市| 淮安市| 浮山县| 额济纳旗| 本溪市| 拜泉县| 清水河县| 忻州市| 哈密市| 察雅县| 蒙自县| 广元市| 和林格尔县| 宜君县| 庆元县| 普洱| 博兴县| 昌吉市|