coursera machine learning python

, 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

use of MATLAB. *.4.gradientdescent.mfunction [Theta, j_history] =gradientdescent (X, y, theta, Alpha, num_iters)%gradientdescent performs gradient descent to learn theta% theta = gradientdescent (X, y, theta, Alpha, num_iters) up Dates theta by% taking num_iters gradient steps with learning rate alpha% Initialize Some useful valuesm= Length (y);%Number of training examplesj_history= Zeros (Num_iters,1); forITER =1: Num_iters% ======================

would the Vectorize this code to run without all for loops? Check all the Apply. A: v = A * x; B: v = Ax; C: V =x ' * A; D: v = SUM (A * x); Answer: A. v = a * x; v = ax:undefined function or variable ' Ax '. 4.Say you has a vectors v and Wwith 7 elements (i.e., they has dimensions 7x1). Consider the following code: z = 0; For i = 1:7 Z = z + V (i) * W (i) End Which of the following vectorizations correctly compute Z? Check all the Apply.

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

-Gradient descent for linear regressionHere we apply the gradient descent algorithm to the linear regression model, we first review the gradient descent algorithm and the linear regression model:We then expand the slope of the gradient descent algorithm to the partial derivative:In most cases, the linear regression model cost function is shaped like a convex body, so the local minimum value is equivalent to the global minimum:The following is the entire convergence and parameter determination pr

Overview photo OCR problem Description and Pipeline sliding Windows getting Lots of data and Artificial data ceiling analysis:what part of the Pipeline to work on Next Review Lecture Slides Quiz:Application:Photo OCR Conclusion Summary and Thank You Log 4/20/2017:1.1, 1.2; Note Ocr? ... Coursera-

-Normal equationSo far, the gradient descent algorithm has been used in linear regression problems, but for some linear regression problems, the normal equation method is a better solution.The normal equation is solved by solving the following equations to find the parameters that make the cost function least:Assuming our training set feature matrix is x, our training set results are vector y, then the normal equation is used to solve the vector:The following table shows the data as an example:T

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

assigned to others, then the median is the score of each job. UW's two courses are videos of UW's class directly, and the homework of machine correction is boring. Therefore, Coursera's courses are also uneven and need to be screened, but the overall quality is still relatively high. I plan to take some social science courses now. I am waiting for the course class to begin with an English writing course and a philosophical entry-level course. The for

Use Python to master machine learning in four steps and python to master machines in four steps To understand and apply machine learning technology, you need to learn Python or R. Both

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/

This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python machine learning time Guide. Learn the workflow of machine Learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'

[Machine learning algorithm-python implementation] matrix denoising and normalization, python Machine Learning1. The background project is required. We plan to use python to implement matrix denoising and normalization. The numpy

Python Machine Learning Theory and Practice (6) Support Vector Machine and python Learning Theory In the previous section, the theory of SVM is basically pushed down, and the goal of finding the maximum interval is finally convert

This article focuses on the contents of the 1.2Python libraries and functions in the first chapter of the Python Machine learning Time Guide. Learn the workflow of machine learning.I. Acquisition and inspection of dataRequests getting dataPandans processing Data1 ImportOS2 ImportPandas as PD3 ImportRequests4 5PATH = R'

Python Machine Learning Theory and Practice (5) Support Vector Machine and python Learning Theory Support vector machine-SVM must be familiar with