coursera gcp

-Learning RateIn the gradient descent algorithm, the number of iterations required for the algorithm convergence varies according to the model. Since we cannot predict in advance, we can plot the corresponding graphs of iteration times and cost functions to observe when the algorithm tends to converge.Of course, there are some ways to automatically detect convergence, for example, we compare the change value of a cost function with a predetermined threshold, such as 0.001, to determine convergen

-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

-Gradient descentThe gradient descent algorithm is an algorithm for calculating the minimum value of a function, and here we will use the gradient descent algorithm to find the minimum value of the cost function.The idea of a gradient descent is that we randomly select a combination of parameters and calculate the cost function at the beginning, and then we look for the next combination of parameters that will reduce the value of the cost function.We continue this process until a local minimum (

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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% ====================== YOUR CODE here ======================% instru

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-machine Learning, Stanford:w

I've been procrastinating for the last time, and I'm going to keep it up today. Programming Title #: Calculating the sum of the edge elements of a matrix Source: POJ (Coursera statement: The exercises completed on POJ will not be counted into Coursera's final results. ) Note: Total time limit: 1000ms memory limit: 65536kB description Enter an integer matrix to compute the sum of elements at the edge of the matrix. The elements of the so-called matrix

Week 3 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 Assume you are using a Unigram language model to calculate the probabilities of phrases. Then, the probabilities of generating the phrases "study text mining" and "text mining study" are not equal, i

Week 3 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 are given a vocabulary composed of only three words: "text", "mining", and "the". Below are the probabilities of two of this three words given by a Unigram model: Word Probability Text 0.4 M

First, how to learn a large-scale data set?In the case of a large training sample set, we can take a small sample to learn the model, such as m=1000, and then draw the corresponding learning curve. If the model is found to be of high deviation

Welcome and Introductionoverviewreadinglog 9/9 videos and quiz completed; 10/29 Review; Note1.1 Welcome 1) What are machine learning? Machine learning are the science of getting compters to learn, without being

 II. Linear Regression with one Variable (Week 1)-Model representationIn the case of previous predictions of house prices, let's say that our training set of regression questions (Training set) looks like this:We use the following notation to

This week's programming work is mainly two-part content.1.k-means Clustering.2.PCA (Principle Component analys) principal component analysis.The main method is to compress the image by clustering the image, and then it is found that PCA can compress

Gradient descent algorithm minimization of cost function J gradient descent Using the whole machine learning minimization first look at the General J () function problem We have J (θ0,θ1) we want to get min J (θ0,θ1) gradient drop for more general

Neural networks:learning Last week's course learned the neural network forward propagation algorithm, this week's course mainly lies in the neural network reverse renewal process. 1.1 Cost function Let's recall the value function of logistic

Deep Learning & art:neural Style Transfer Welcome to the second assignment of this week. In this assignment, you'll learn about neural Style Transfer. This algorithm is created by Gatys et al. (https://arxiv.org/abs/1508.06576). in this assignment,

5.1 Section cost FunctionThe cost function of a neural network.Review some of the concepts in neural networks:L the total number of layers of the neural network.Number of units of the SL-L layer (excluding deviation units).Category 2 Classification

You can access the Google drive containing all of the current and in-progress lecture slides for this course through the L Ink below. Lecture Slides You could find it helpful to either bookmark this page or download the slides for easy

You can access the Google drive containing all of the current and in-progress lecture slides for this course through the L Ink below. Lecture Slides You could find it helpful to either bookmark this page or download the slides for easy

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learning algorithm is learning is not how the model behaves on