多层前馈神经网络:BP算法

Posted by tianhaoo on May 21, 2019 本文阅读量

人工神经网络的基本单位——感知机(神经元)(perceptron)

perceptron

通过训练,调整权重(w)和阈值(b),我们就可以根据神经元做出正确的决策

参考 https://zh.wikipedia.org/zh-hans/%E4%BA%BA%E5%B7%A5%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C

神经网络的全部分类(截至2016年)

all nn

参考 https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463

前馈神经网络(Feedforward neural network)

The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network.

参考 https://en.wikipedia.org/wiki/Feedforward_neural_network

BP算法

误差后向传播(Error Back Propagation)

工作信号正向传递