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Coursera-machine Learning, Stanford:week 5

Overview Cost Function and BackPropagation Cost Function BackPropagation algorithm BackPropagation Intuition Back propagation in practice Implementation Note:unrolling Parameters Gradient Check Random initialization Put It together Application of Neural Networks Autonomous Driving Review Log 2/10/2017:all the videos; Puzzled about Backprogation 2/11/2017:reviewed backpropaga

Python Learning Note--coursera

Someting about Lists mutation1 ###################################2 #Mutation vs. Assignment3 4 5 ################6 #Look alike, but different7 8A = [4, 5, 6]9b = [4, 5, 6]Ten Print "Original A and B:", A, b One Print "is they same thing?"+ F isb A -A[1] = 20 - Print "New A and B:", A, b the Print - - ################ - #aliased + -c = [4, 5, 6] +D =C A Print "Original C and D:", C, D at Print "is they same thing?"+ D isD - -C[1] = 20 - Print "New C and D:", C, D - Print - in ##############

Coursera Python Learning Summary

points of mini project are translated, Then translate the Mini project implementation steps, not a one-time full translation, take too long, the previous translation may forget, and the translation may not be accurate, and sometimes to see the original text. Complete a paragraph and translate the next paragraph, step by step. Do not translate all, some do not help to complete the task can not translate, save time. 4. Selective translation of code clinic,5. If you get stuck, search for keywords

UIUC University Coursera Course text retrieval and Search Engines:week 2 Practice University

Week 2 Practice quizhelp Center Warning:the hard deadline has passed. You can attempt it, but and you won't be. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify this answers here are I own work. Question 1 Suppose a query has a total of 5 relevant documents in a collection of documents. System A and System B have each retrieved, and the relevance status of the ranked lists is shown below: Sys

UIUC University Coursera Course text retrieval and Search Engines:week 4 Practice University

Week 4 Practice quizhelp Center The Warning:the hard deadline has passed. You can attempt it, Butyou won't get credit for it. You are are welcome to try it as a learning exercise. In accordance with the Coursera Honor Code, I certify This answers here are I own work. Question 1 Can a crawler that only follows hyperlinks identify hidden pages, does not have any incoming links? No Yes question 2 after obtaining the chunk's handle and locations from th

Machine Learning| Andrew ng| Coursera Wunda Machine Learning Notes

continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Text detection Character segmentation Character classification Using s

Note for Coursera "Machine learning" 1 (1) | What are machine learning?

What are machine learning?The definitions of machine learning is offered. Arthur Samuel described it as: "The field of study that gives computers the ability to learn without being explicitly prog Rammed. " This was an older, informal definition.Tom Mitchell provides a more modern definition: 'a computer program was said to learn from experience E with R Espect to some class of tasks T and performance measure P, if it performance at tasks in T, as measured By P, improves with experience E."Examp

"Original Hadoop&spark Hands-on 5" Spark Basics Starter, cluster build and Spark Shell

Introduction to spark Basics, cluster build and Spark ShellThe main use of spark-based PPT, coupled with practical hands-on to enhance the concept of understanding and practice.Spark Installation DeploymentThe theory is almost there, and then the actual hands-on experiment:Exercise 1 using Spark Shell (native mode) to

[Spark Asia Pacific Research Institute Series] the path to spark practice-Chapter 1 building a spark cluster (step 4) (7)

Step 4: build and test the spark development environment through spark ide Step 1: Import the package corresponding to spark-hadoop, select "file"> "project structure"> "Libraries", and select "+" to import the package corresponding to spark-hadoop: Click "OK" to confirm: Click "OK ": After idea

Spark Streaming (top)--real-time flow calculation spark Streaming principle Introduction

1. Introduction to Spark streaming 1.1 Overview Spark Streaming is an extension of the Spark core API that enables the processing of high-throughput, fault-tolerant real-time streaming data. Support for obtaining data from a variety of data sources, including KAFK, Flume, Twitter, ZeroMQ, Kinesis, and TCP sockets, after acquiring data from a data source, you can

Locally developed spark code uploads the spark Cluster service and runs it (based on the Spark website documentation)

Open idea under the SRC under main under Scala right click to create a Scala class named Simpleapp, the content is as followsImportOrg.apache.spark.SparkContextImportOrg.apache.spark.sparkcontext._ImportOrg.apache.spark.SparkConfObjectSimpleapp{defMain(Args:array[string]) {ValLogFile ="/home/spark/opt/spark-1.2.0-bin-hadoop2.4/readme.md"//should be some file on your system Valconf =NewSparkconf (). Setap

Coursera-an Introduction to Interactive programming in Python (Part 1)-mini-project #4-"Pong"

(Paddle2_pos,1,'Blue',' White') #determine whether paddle and ball collide ifBall_pos[0] Pad_width:ifBALL_POS[1] >= paddle1_pos[0][1] andBALL_POS[1] ]: Spawn_ball (right)Else: Score2+ = 1ifBall_pos[0] >= Width-pad_width-Ball_radius:ifBALL_POS[1] >= paddle2_pos[0][1] andBALL_POS[1] ]: Spawn_ball (left)Else: Score1+ = 1#Draw scoresCanvas.draw_text (str (score1), [WIDTH/2-40, 40], 30,' White') Canvas.draw_text (str (score2), [WIDTH/2 + 20, 40], 30,' White')defKeyDown (key):GlobalPaddle1_vel,

Coursera-an Introduction to Interactive programming in Python (Part 1)-mini-project #3-"Stopwatch:the Game"

(stop_num)#define event handlers for buttons; "Start", "Stop", "Reset"defStart_handler (): Timer.start ()defStop_handler (): Timer.stop ()defReset_handler (): Timer.stop ()GlobalTGlobalt_str Reset_score ()#Define event handler for timer with 0.1 sec intervaldefTimer_handler ():GlobalTGlobalT_str T= t + 1T_str=format (t)defTimer_score_handler (): Update_score ()#Define Draw HandlerdefDraw_handler (Canvas): Canvas.draw_text (t_str, Position,36," White") Canvas.draw_text (SCORE_STR, [160, 20], 16,

What is the essence of scala pattern matching? -Starting from responsive programming of Coursera

We recommend the responsive programming course on Coursera, an advanced Scala language course. At the beginning of the course, we proposed an Application Scenario: constructing a JSON string. If you do not know the JSON string, you can simply Google it. To do this, we define the following classes abstract class JSON case class JSeq(elems: List[JSON]) extends JSON case class JObj(bindings: Map[String, JSON]) extends JSON case class JNum(num: Double) e

Coursera University program design and algorithm special courses perfect coverage

#include using namespacestd;/*int Wanmeifugai (int n) {if (n%2) {return 0; } else if (n==2) {return 3; }else if (n = = 0) return 1; else return (3*3) *wanmeifugai (n-4);}*///The following is a reference to the online program/*Ideas: Citation:http://m.blog.csdn.net/blog/njukingway/20451825First: F (n) = 3*f (n-2) + ... f (n) = 3*f (n-2) + 2*f (n-4) +....//just now our recursion is pushed in the smallest unit (3 blocks), but there are large units of small units (6, 9, 12 blocks, etc.) There

Coursera-miniproject stopwatch task Summary

y += 1 timer.stop() elif timer.is_running(): y += 1 timer.stop() def reset(): global t, x, y t = 0 x = 0 y = 0 timer.stop()# define event handler for timer with 0.1 sec intervaldef tick(): global t t += 1#不需要return# define draw handlerdef draw(canvas): canvas.draw_text(format(t), [80, 120], 50, "White") canvas.draw_text(str(x) + "/" + str(y), [220, 30], 35, "Green")# create framef = simplegui.create_frame("Stopwatch", 300, 200)

[Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford machine learning.

Week 2 gradient descent for multiple variables [1] multi-variable linear model cost function Answer: AB [2] feature scaling feature Scaling Answer: d 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: 【] Answer: [Original] Andrew Ng chose to fill in the blanks in Coursera for Stanford machine learning.

Coursera Machine Learning Chapter 9th (UP) Anomaly Detection study notes

m>=10n and uses multiple Gaussian distributions.In practical applications, the original model is more commonly used, the average person will manually add additional variables.If the σ matrix is found to be irreversible in practical applications, there are 2 possible reasons for this:1. The condition of M greater than N is not satisfied.2. There are redundant variables (at least 2 variables are exactly the same, XI=XJ,XK=XI+XJ). is actually caused by the linear correlation of the characteristic

Stanford Coursera Machine Learning Programming Job Exercise 5 (regularization of linear regression and deviations and variances)

different lambda, the calculated training error and cross-validation error are as follows:Lambda Train error Validation error 0.000000 0.173616 22.066602 0.001000 0.156653 18.597638 0.003000 0.190298 19.981503 0.010000 0.221975 16.969087 0.030000 0.281852 12.829003 0.100000 0.459318 7.587013 0.300000 0.921760 1.000000 2.076188 4.260625 3.000000 4.901351 3.822907 10.000000 16.092213 9.945508The graphic is represented as follows:As

Ntu-coursera machine Learning: Noise and Error

, the weight of the high-weighted data is increased by 1000 times times the probability, which is equivalent to replication. However, if you are traversing the entire test set (not sampling) to calculate the error, there is no need to modify the call probability, just add the weights of the corresponding errors and divide by N. So far, we have expanded the VC Bound, which is also set up on the issue of multiple classifications!SummaryFor more discussion and exchange on machine learning, please

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