Logistic regression

　　

Cost function for logistic regression

　　

Gradient Descent

　　

　　

　　

　　

　　

接下来主要讲 Vectorization

　　

Logistic Regression 的向量实现　　

　　

Vectorizing LR Gradient output

　　

Python/Numpy and Jupyter Notebook

　　

　　

上图中 axis=0 表示竖直方向，axis=1 是水平方向

Coursera, Deep Learning 1, Neural Networks and Deep Learning - week2, Neural Networks Basics的更多相关文章

  1. 【DeepLearning学习笔记】Coursera课程《Neural Networks and Deep Learning》——Week2 Neural Networks Basics课堂笔记

    Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week2 Neural Networks Basics 2.1 ...

  2. 【DeepLearning学习笔记】Coursera课程《Neural Networks and Deep Learning》——Week1 Introduction to deep learning课堂笔记

    Coursera课程<Neural Networks and Deep Learning> deeplearning.ai Week1 Introduction to deep learn ...

  3. 课程一(Neural Networks and Deep Learning)，第四周（Deep Neural Networks） —— 3.Programming Assignments: Deep Neural Network - Application

    Deep Neural Network - Application Congratulations! Welcome to the fourth programming exercise of the ...

  4. 课程一(Neural Networks and Deep Learning)，第二周（Basics of Neural Network programming）—— 4、Logistic Regression with a Neural Network mindset

    Logistic Regression with a Neural Network mindset Welcome to the first (required) programming exerci ...

  5. Neural Networks and Deep Learning

    Neural Networks and Deep Learning This is the first course of the deep learning specialization at Co ...

  6. [C3] Andrew Ng - Neural Networks and Deep Learning

    About this Course If you want to break into cutting-edge AI, this course will help you do so. Deep l ...

  7. 《Neural Networks and Deep Learning》课程笔记

    Lesson 1 Neural Network and Deep Learning 这篇文章其实是 Coursera 上吴恩达老师的深度学习专业课程的第一门课程的课程笔记. 参考了其他人的笔记继续归纳 ...

  8. 第四节，Neural Networks and Deep Learning 一书小节(上)

    最近花了半个多月把Mchiael Nielsen所写的Neural Networks and Deep Learning这本书看了一遍,受益匪浅. 该书英文原版地址地址:http://neuralne ...

  9. 课程四(Convolutional Neural Networks)，第二 周（Deep convolutional models: case studies） —— 0.Learning Goals

    Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the ...

  10. 课程一(Neural Networks and Deep Learning)，第三周（Shallow neural networks）—— 3.Programming Assignment : Planar data classification with a hidden layer

    Planar data classification with a hidden layer Welcome to the second programming exercise of the dee ...

随机推荐

  1. js 获取当前时间 年月日

    var datetime = new Date(); var year = datetime.getFullYear(); var month = datetime.getMonth() + 1 &l ...

  2. 使用Spring Cloud连接不同服务

    http://www.infoq.com/cn/articles/spring-cloud-service-wiring 主要结论 Spring Cloud为微服务系统中相互依赖的服务提供了丰富的连接 ...

  3. Linux下VMware在更新完内核无法启动

    该问题尚未解决,我已经换Oracle VM VirtualBox

  4. 面试集——redis

    背景:该贴主要用来记面试过程中redis相关的问题,方便后期回顾. 为什么说Redis是单线程的以及Redis为什么这么快! https://blog.csdn.net/xlgen157387/art ...

  5. Mock4 moco框架中如何加入cookies

    新建一个 startupWithCookies.json,因为cookies也是请求当中带的,所以,要写在request里面,cookies是k-v的形式,就拿登陆来说吧,登陆以后会的cookies, ...

  6. C connect实现Timeout效果（Windows）

    int SocketClient::connectTimeOutForWin(SOCKET &connect_fd, const int &timeout, const sockadd ...

  7. java键盘中输入q退出

    用 java.util.Scanner 如下 public static void main(String[] args) { while(true){ Scanner scan = new Scan ...

  8. (sort)P1068 分数线划定 洛谷

    题目描述 世博会志愿者的选拔工作正在 A 市如火如荼的进行.为了选拔最合适的人才,AA市对 所有报名的选手进行了笔试,笔试分数达到面试分数线的选手方可进入面试.面试分数线根 据计划录取人数的150\% ...

  9. M1-Flask-Day2

    内容概要: 1.flask - 蓝图 - 中间件 - 闪现 2.扩展 - session - wtfrom 3.上下文管理 - local-threading 4.websocket - 轮训 - 长 ...

  10. Tensorflow做阅读理解与完形填空

    catalogue . 前言 . 使用的数据集 . 数据预处理 . 训练 . 测试模型运行结果: 进行实际完形填空 0. 前言 开始写这篇文章的时候是晚上12点,突然想到几点新的理解,赶紧记下来.我们 ...