Neural Networks and Deep Learning 课程笔记(第二周)神经网络的编程基础 (Basics of Neural Network programming) 总结 一、处理数据 1.1 向量化(vectorization) (height, width, 3) ===> 展开shape为(heigh*width*3, m)的向量 1.2 特征归一化(Normalization) 一般数据,使用标准化(Standardlization), z(i) = ...
[Deep Learning] 神经网络编程基础 (Basics of Neural Network Programming) - 逻辑回归-梯度下降-计算图 在神经网络中,假如有m个训练集,我们想把他们加入训练,第一个想到得就是用一个for循环来遍历训练集,从而开始训练。但是在神经网络中,我们换一个计算方法,这就是前向传播和反向传播。 对于逻辑回归,就是找出...
Jupyter/iPython Notebooks快速入门(Quick tour of Jupyter/iPython Notebooks) logistic 损失函数的解释(Explanation of logistic regression cost function) 回想一下,在逻辑回归中,需要预测的结果$\hat{y}$,可以表示为$\hat{y}=\sigma(w^{T}x+b)$,$\sigma$是我们熟悉的$S$型函数 $\sigma(z)=\sigma(w{T...
Neural Networks and Deep Learning 课程笔记(第二周)神经网络的编程基础 (Basics of Neural Network programming) 总结 一、处理数据 1.1 向量化(vectorization) (height, width, 3) ===> 展开shape为(heigh*width*3, m)的向量 1.2 特征归一化(Normaliz... ...
2.1 二分类(Binary Classification) 用(x,y)来表示一个单独的样本,x代表n_x维的特征向量,y标签(输出结果)只能为0或1,X.shape 为(n_x, m);Y.shape 为(1, m); 2.2 逻辑回归(Logistic Regression) 逻辑回归是一个用于二分类(binary classification)的算法,使y的值在0和1之间,将线性函数转换为非线性函数...
第二周:神经网络的编程基础(Basics of Neural Network programming) 2.1 二分类(Binary Classification) 这周我们将学习神经网络的基础知识,其中需要注意的是,当实现一个神经网络的时候,我们需要知道一些非常重要的技术和技巧。例如有一个包含m个样本的训练集,你很可能习惯于用一个for循环来遍历训练集中的每个样本,但...
slope of function Computation Graph Vectorization 用python的np.dot运行比for loop速度快300多倍 (当纬度为10000) Vetorizing Logistic Regression Python numpy 的传播性 Broadcasting in Python 在numpy中.sum()函数 默认参数为axis=none .sum(axis=none) 把矩阵所有值相加 .sum(axis=0)把矩阵按行相加 .sum(ax...
第二周:神经网络的编程基础(Basics of Neural Network programming) 2.1 二分类(Binary Classification) 2.2 逻辑回归(Logistic Regression) 2.3 逻辑回归的代价函数(Logistic Regression Cost Function) 2.4 梯度下降法(Gradient Descent) 2.5 导数(Derivatives) ...
Multiple Java technologies exist for providing data exchange among computers in a network. Java provides classes for network programming in the package java.net. This chapter shows how to read data from the Internet using the class URL as well as direct socket-to-socket programming. The chapter ...
"If you do decide to usesockets, bind the 'server' socket to 'localhost'. On most platforms, this will take a shortcut around a couple of layers of network code and be quite a bit faster." In Python 3, all strings areUnicode. For more info, visitCharacter Encoding. ...