课程一(Neural Networks and Deep Learning),第三周(Shallow neural networks)—— 2、Practice Questions

-----------------------------------------------------------------------------------------------------------------------

-----------------------------------------------------------------------------------------------------------------------

-----------------------------------------------------------------------------------------------------------------------

import numpy as np
A=np.random.randn(4, 3)
B=np.sum(A, axis=1, keepdims=True) # axis=1时,按照行计算; axis=0时,按照列计算
print("A="+str(A))
print("B="+str(B)) result:
A=[[-0.02149271 -1.0911196 -0.63240592]
[-0.11458854 -0.18210595 0.82210656]
[ 0.39105364 -0.97201463 -0.71820102]
[ 0.30185741 -0.50767254 -0.73277816]]
B=[[-1.74501822]
[ 0.52541207]
[-1.29916201]
[-0.93859329]]
-----------------------------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------------------------------------------

-------------------------------------------------------------------------------------------------------------------

-------------------------------------------------------------------------------------------------------------------


-------------------------------------------------------------------------------------------------------------------

-------------------------------------------------------------------------------------------------------------------
答案仅供参考
课程一(Neural Networks and Deep Learning),第三周(Shallow neural networks)—— 2、Practice Questions的更多相关文章
- 吴恩达《深度学习》-第一门课 (Neural Networks and Deep Learning)-第三周:浅层神经网络(Shallow neural networks) -课程笔记
第三周:浅层神经网络(Shallow neural networks) 3.1 神经网络概述(Neural Network Overview) 使用符号$ ^{[
- 【面向代码】学习 Deep Learning(三)Convolution Neural Network(CNN)
========================================================================================== 最近一直在看Dee ...
- 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 ...
- 课程一(Neural Networks and Deep Learning),第一周(Introduction to Deep Learning)—— 2、10个测验题
1.What does the analogy “AI is the new electricity” refer to? (B) A. Through the “smart grid”, AI i ...
- 课程一(Neural Networks and Deep Learning),第一周(Introduction to Deep Learning)—— 1、经常提及的问题
Frequently Asked Questions Congratulations to be part of the first class of the Deep Learning Specia ...
- 课程一(Neural Networks and Deep Learning),第二周(Basics of Neural Network programming)—— 3、Python Basics with numpy (optional)
Python Basics with numpy (optional)Welcome to your first (Optional) programming exercise of the deep ...
- 课程一(Neural Networks and Deep Learning),第一周(Introduction to Deep Learning)—— 0、学习目标
1. Understand the major trends driving the rise of deep learning.2. Be able to explain how deep lear ...
- 课程一(Neural Networks and Deep Learning),第二周(Basics of Neural Network programming)—— 0、学习目标
1. Build a logistic regression model, structured as a shallow neural network2. Implement the main st ...
- 课程一(Neural Networks and Deep Learning),第二周(Basics of Neural Network programming)—— 2、编程作业常见问题与答案(Programming Assignment FAQ)
Please note that when you are working on the programming exercise you will find comments that say &q ...
- 课程一(Neural Networks and Deep Learning),第二周(Basics of Neural Network programming)—— 1、10个测验题(Neural Network Basics)
--------------------------------------------------中文翻译---------------------------------------------- ...
随机推荐
- 【服务器】Nginx文件配置
nginx.conf文件 #运行用户 user nobody; #启动进程,通常设置成和cpu的数量相等 worker_processes 1; #全局错误日志及PID文件 #error_log lo ...
- laravel 5.1 简单配置例子
这里演示5.1版本 一.数据库配置 .env文件(也可以直接修改config/database.php) DB_HOST=localhost DB_DATABASE=test //数据库名称 DB_ ...
- Java交流分享(522818473)
今天来分享哈自己的技术交流群,记得还是刚接触Java建立的群,那时候学习Java很有动力,经常和群友谈论问题,现在都专注公司业务和技术这一块,很多后端框架都没用除了restful,其他都是封装的,不过 ...
- XML xmlns
xmlns xml namespaces 参考 http://www.w3school.com.cn/tags/tag_prop_xmlns.asp http://www.w3school.com.c ...
- 2.2.9静态同步synchronized方法与synchronized(class)代码块
关键字synchronized还可以应用在static静态方法上,这样写那是对当前的*.java文件对应的class类进行持锁, 测试如下 package com.cky.bean; /** * Cr ...
- poj 3258 3273
poj3258 题目 (最大化最小值)(最小值最大化) 题意:牛要到河对岸,在与河岸垂直的一条线上,河中有N块石头,给定河岸宽度L,以及每一块石头离牛所在河岸的距离,现在去掉M块石头,要求去掉M块石 ...
- rsync+sersync+inotify实现服务器间文件同步之一
rsync+sersync+inotify实现服务器间文件同步之一:rsync安装配置 2013年12月14日 ⁄ Linux管理, 服务器集群技术 ⁄ 共 4925字 ⁄ rsync+sersync ...
- 分形之希尔伯特-皮亚诺(Hilbert-Peano)曲线
1890年,意大利数学家皮亚诺(Peano G)发明能填满一个正方形的曲线,叫做皮亚诺曲线.后来,由希尔伯特作出了这条曲线,又名希尔伯特曲线.Hilbert-Peano曲线是一种分形图形,它可以画得无 ...
- title
事实上 @import url(http://i.cnblogs.com/Load.ashx?type=style&file=SyntaxHighlighter.css);@import ur ...
- Word 如何实现表格快速一分为二
将光标定位在分断处,按快捷键“Ctrl+Shift+Enter“