coursera deep learning cost

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Notes | Wunda Coursera Deep Learning Study notes

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 machi

Neural network and deep learning programming exercises (Coursera Wunda) (3)

=parameters[' W2 '] b2=parameters[' B2 '] for I in range (0,num_iterations): A1,c Ache1=linear_activation_forward (x,w1,b1, ' Relu ') a2,cache2=linear_activation_forward (a1,w2,b2, ' sigmoid ') c Ost=compute_cost (a2,y) da2=-(Np.divide (Y,A2)-np.divide (1-Y,1-A2)) Da1,dw2,db2=linear_activaTion_backward (da2,cache2, ' sigmoid ') da0,dw1,db1=linear_activation_backward (da1,cache1, ' Relu ') grads[' dW1 ' ]=DW1 grads[' db1 ']=db1 grads[' dW2 ']=dw2 grads[' DB2 ']=db2 parameters=update_parameters (p

Coursera Deep Learning Fourth lesson accumulation neural network fourth week programming work Art Generation with neural Style transfer-v2

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. ( in this assignment, you'll:-Implement the neural style transfer algorithm-Generate novel artistic images using your algorithm Most of the algorithms you ' ve studied optimize a

Coursera open course notes: "Advice for applying machine learning", 10 class of machine learning at Stanford University )"

networks and overfitting: The following is a "small" Neural Network (which has few parameters and is easy to be unfitted ): It has a low computing cost. The following is a "big" Neural Network (which has many parameters and is easy to overfit ): It has a high computing cost. For the problem of Neural Network overfitting, it can be solved through the regularization (λ) method. References: Machine

[Machine Learning] Coursera notes-Support Vector machines

friends, but also hope to get the high people of God's criticism!        Preface  [Machine Learning] The Coursera Note series was compiled with notes from the course I studied at the Coursera learning (Andrew ng teacher). The content covers linear regression, logistic regression, Softmax regression, SVM, neural networ

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: Tex

"MATLAB" machine learning (Coursera Courses Outline & Schedule)

learning Machine Learning System Design Programming Exercise 5:regularized Linear Regression and Bias v.s. VarianceBest and Most Recent SubmissionScore100 / 100 points earned PASSEDSubmitted on 11 七月 2015 在 3:28 凌晨Part Name Score1 Regularized linear regression cost function 25 / 252 Regularized linear regression gradient 25 / 253

Neural Network jobs: NN Learning Coursera machine learning (Andrew Ng) WEEK 5

In Week 5, the job requires supervised learning (suoervised learning) to recognize Arabic numerals through a neural network (NN) for multi-classification logistic regression (multi-class logistic REGRESSION). The main purpose of the job is to feel how to find the cost function in the NN and the derivative value of each parameter (THETA) in its hypothetical functi

[Machine Learning] Coursera ml notes-Logistic regression (logistic Regression)

IntroductionThe Machine learning section records Some of the notes I've learned about the learning process, including linear regression, logistic regression, Softmax regression, neural networks, and SVM, and the main learning data from Standford Andrew Ms Ng's tutorials in Coursera and online courses such as UFLDL Tuto

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

This paper uses the regularization linear regression model pre-flow (water flowing out of dam) according to the water storage line (water level) of the reservoir, then the Debug Learning Algorithm and discusses the influence of deviation and variance on the linear regression model.① visualizing datasetsThe data set for this job is divided into three parts:Training set (training set), sample matrix (Training Set): X, results label (label of result) Vec

Coursera Course "Machine learning" study notes (WEEK1)

This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this course to find a leak. The current plan is to see the end of the neural network, the back is not necessarily seen.Of course, look at the process is still to do the no

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

Coursera Machine Learning Study notes (vi)

-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 w

Operating system Learning notes----process/threading Model----Coursera Course notes

Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introducedThe composition of the threadImplementatio

Coursera Machine Learning second week programming job Linear Regression

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% ======================

Coursera Open Class Machine Learning: Linear Regression with multiple variables

regression. The root number can also be selected based on the actual situation.Regular Equation In addition to Iteration Methods, linear algebra can be used to directly calculate $ \ matrix {\ Theta} $. For example, four groups of property price forecasts: Least Squares $ \ Theta = (\ matrix {x} ^ t \ matrix {x}) ^ {-1} \ matrix {x} ^ t \ matrix {y} $Gradient Descent, advantages and disadvantages of regular equations Gradient Descent: Desired stride $ \ Alpha $; Multiple iterations are requ

Coursera Machine Learning Study notes (10)

-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 valu

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

"Coursera-machine learning" Linear regression with one Variable-quiz

, i.e., all of our training examples lie perfectly on some straigh T line. If J (θ0,θ1) =0, that means the line defined by the equation "y=θ0+θ1x" perfectly fits all of our data. For the To is true, we must has Y (i) =0 for every value of i=1,2,..., m. So long as any of our training examples lie on a straight line, we'll be able to findθ0 andθ1 so, J (θ0,θ1) =0. It is not a necessary that Y (i) =0 for all of our examples. We can perfectly predict the value o

Ntu-coursera machine Learning: Noise and Error

false reject called false refusal, that is, the original legal identification is illegal, and the other is called false accept, that is, illegal identification is legal.Imagine an application, a supermarket through the fingerprint identification of members, if it is a member to give a certain discount. If a member is wrongly rejected, he is likely to be angry and refuse to come to the supermarket because he has not enjoyed the rights he should have, and the supermarket will lose a stable source

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