Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets(解释口译) and understand your voice commands, it is likely that a neural network is helping to understand your speech; wh…
一.Non-linear Hypotheses 线性回归和逻辑回归在特征很多时,计算量会很大. 一个简单的三层神经网络模型: \[a_i^{(j)} = \text{"activation" of unit $i$ in layer $j$}$$$$\Theta^{(j)} = \text{matrix of weights controlling function mapping from layer $j$ to layer $j+1$} \] 其中:$$a_1^{(2)} = g…
3. Model Representation I 1 神经网络是在模仿大脑中的神经元或者神经网络时发明的.因此,要解释如何表示模型假设,我们不妨先来看单个神经元在大脑中是什么样的. 我们的大脑中充满了如上图所示的这样的神经元,神经元是大脑中的细胞.其中有两点值得我们注意,一是神经元有像这样的细胞主体(Nucleus),二是神经元有一定数量的输入神经和输出神经.这些输入神经叫做树突(Dendrite),可以把它们想象成输入电线,它们接收来自其他神经元的信息.神经元的输出神经叫做轴突(Axon),…
Neural Network Motivations 想要拟合一条曲线,在feature 很多的情况下,feature的组合也很多,在现实中不适用,比如在computer vision问题中feature就太多了. Applications cost function and BP Gradient Checking https://www.coursera.org/learn/machine-learning/supplement/pjdBA/backpropa…
