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
- Win7 VS2013环境使用cuda_7.5.18
首先得吐槽下VS2015出来快一年了CUDA居然还不支持,没办法重装系统刚从2013升到2015,还得再装回一个2013用,只为学习CUDA... 然后安装的时候,如果你选择自定义组件安装,注意不要改 ...
- C++STL容器重点
string 查找和替换 vector 删除
- 软件推荐-国内参数优化软件:1stOpt - First Optimizationg
首页:http://www.7d-soft.com/index.htm 4.0新功能 (预定2010年8月6日): 1:支持复数拟合.复数方程组计算: 2:支持微分方程拟合求解: 3:通用全局优化求解 ...
- python中的分号
很多编程语言是以分号作为一行代码的的结束标志,但是在Python中不是这样的,而是靠缩进来识别程序结构. Python中一行代码以分号结束,并不是必须的,准确来说是不被推荐的,因为加上分号就是画蛇添足 ...
- Linux top命令总结
一:在bash里输入top后出现的数据当中目前自己容易理解的有 1.task:中的 num total表示总共有num个进程:num running是正在运行的进程数:num sleeping是正在休 ...
- 关于内存类型 UDIMM、RDIMM、LRDIMM 的学习结论(转)
随着内存技术不断发展,服务器上内存的容量.密度和速度也越来越高.目前在市场上出现的内存条最高密度可以做到每条内存条 4 个 Rank,容量达到 32GB/条,最高速度达到 1.6GHz.高密度高频率也 ...
- [小结]了解innodb锁
原创文章,会不定时更新,转发请标明出处:http://www.cnblogs.com/janehoo/p/5603983.html 背景介绍: innodb的锁分两类:lock和latch. 其中la ...
- 安装postgis,使用postgis导入shapefile的步骤总结
最近在做开源WebGIS方面的工作,要使用postgis导入shapefile数据.难点在安装过程和导入时命令行参数的使用,以下分别作个介绍,希望对大家有点用 一.安装postgis (1)首先到po ...
- Windows 8.1常见问题
Windows 8.1常见问题 1. 我想升级Windows 8.1,但是担心软件.硬件不兼容怎么办? 对于已安装的软件及联机的设备,可以在微软网站上下载Windows 8.1升级助手进行检测,会在检 ...
- 第74讲:从Spark源码的角度思考Scala中的模式匹配
今天跟随王老师学习了从源码角度去分析scala中的模式匹配的功能.让我们看看源码中的这一段模式匹配: 从代码中我们可以看到,case RegisterWorker(id,workerHost,.... ...