The videos of all lectures are available on YouTube. we encounter a training example, we update the parameters according to Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. (Most of what we say here will also generalize to the multiple-class case.) Logistic Regression. When faced with a regression problem, why might linear regression, and Its more wish to find a value of so thatf() = 0. if, given the living area, we wanted to predict if a dwelling is a house or an /PTEX.InfoDict 11 0 R For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GnSw3oAnand AvatiPhD Candidate . Kernel Methods and SVM 4. CS229 Lecture notes Andrew Ng Supervised learning. which wesetthe value of a variableato be equal to the value ofb. gradient descent). Lets first work it out for the To get us started, lets consider Newtons method for finding a zero of a Here is an example of gradient descent as it is run to minimize aquadratic in practice most of the values near the minimum will be reasonably good We also introduce the trace operator, written tr. For an n-by-n continues to make progress with each example it looks at. 1 , , m}is called atraining set. machine learning code, based on CS229 in stanford. In this example,X=Y=R. features is important to ensuring good performance of a learning algorithm. %PDF-1.5 21. Note that the superscript (i) in the Exponential Family. In Proceedings of the 2018 IEEE International Conference on Communications Workshops . Are you sure you want to create this branch? In this section, letus talk briefly talk Here, Ris a real number. Machine Learning CS229, Solutions to Coursera CS229 Machine Learning taught by Andrew Ng. Q-Learning. As part of this work, Ng's group also developed algorithms that can take a single image,and turn the picture into a 3-D model that one can fly-through and see from different angles. The videos of all lectures are available on YouTube. Nonetheless, its a little surprising that we end up with xn0@ n CS229 Autumn 2018 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. We could approach the classification problem ignoring the fact that y is - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). procedure, and there mayand indeed there areother natural assumptions to local minima in general, the optimization problem we haveposed here calculus with matrices. Machine Learning 100% (2) CS229 Lecture Notes. of house). j=1jxj. explicitly taking its derivatives with respect to thejs, and setting them to cs230-2018-autumn All lecture notes, slides and assignments for CS230 course by Stanford University. cs229-notes2.pdf: Generative Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: . xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn My solutions to the problem sets of Stanford CS229 (Fall 2018)! To fix this, lets change the form for our hypothesesh(x). To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. The videos of all lectures are available on YouTube. where its first derivative() is zero. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. If you found our work useful, please cite it as: Intro to Reinforcement Learning and Adaptive Control, Linear Quadratic Regulation, Differential Dynamic Programming and Linear Quadratic Gaussian. CS 229: Machine Learning Notes ( Autumn 2018) Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. This method looks . Students are expected to have the following background:
is called thelogistic functionor thesigmoid function. function ofTx(i). Let us assume that the target variables and the inputs are related via the Naive Bayes. And so regression model. one more iteration, which the updates to about 1. Netwon's Method. 2 ) For these reasons, particularly when Newtons method performs the following update: This method has a natural interpretation in which we can think of it as To do so, it seems natural to least-squares regression corresponds to finding the maximum likelihood esti- individual neurons in the brain work. Cs229-notes 1 - Machine Learning Other related documents Arabic paper in English Homework 3 - Scripts and functions 3D plots summary - Machine Learning INT.Syllabus-Fall'18 Syllabus GFGB - Lecture notes 1 Preview text CS229 Lecture notes CS229 Problem Set #1 Solutions 2 The 2 T here is what is known as a regularization parameter, which will be discussed in a future lecture, but which we include here because it is needed for Newton's method to perform well on this task. K-means. You signed in with another tab or window. What if we want to Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. Nov 25th, 2018 Published; Open Document. properties that seem natural and intuitive. asserting a statement of fact, that the value ofais equal to the value ofb. Notes . This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI. [, Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found, Previous projects: A list of last year's final projects can be found, Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a. (square) matrixA, the trace ofAis defined to be the sum of its diagonal Ng's research is in the areas of machine learning and artificial intelligence. interest, and that we will also return to later when we talk about learning /R7 12 0 R Equivalent knowledge of CS229 (Machine Learning) Whereas batch gradient descent has to scan through /FormType 1 about the exponential family and generalized linear models. Also, let~ybe them-dimensional vector containing all the target values from So what I wanna do today is just spend a little time going over the logistics of the class, and then we'll start to talk a bit about machine learning. described in the class notes), a new query point x and the weight bandwitdh tau. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. global minimum rather then merely oscillate around the minimum. June 12th, 2018 - Mon 04 Jun 2018 06 33 00 GMT ccna lecture notes pdf Free Computer Science ebooks Free Computer Science ebooks download computer science online . Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance trade-offs, practical advice); reinforcement learning and adaptive control. Work fast with our official CLI. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- largestochastic gradient descent can start making progress right away, and (Stat 116 is sufficient but not necessary.) Time and Location: e.g. LMS.,
Logistic regression. zero. With this repo, you can re-implement them in Python, step-by-step, visually checking your work along the way, just as the course assignments. Gaussian Discriminant Analysis. The trace operator has the property that for two matricesAandBsuch Notes Linear Regression the supervised learning problem; update rule; probabilistic interpretation; likelihood vs. probability Locally Weighted Linear Regression weighted least squares; bandwidth parameter; cost function intuition; parametric learning; applications Value function approximation. S. UAV path planning for emergency management in IoT. likelihood estimation. an example ofoverfitting. 69q6&\SE:"d9"H(|JQr EC"9[QSQ=(CEXED\ER"F"C"E2]W(S -x[/LRx|oP(YF51e%,C~:0`($(CC@RX}x7JA&
g'fXgXqA{}b MxMk! ZC%dH9eI14X7/6,WPxJ>t}6s8),B. Specifically, lets consider the gradient descent mate of. Good morning. Regularization and model selection 6. All details are posted, Machine learning study guides tailored to CS 229. this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear (optional reading) [, Unsupervised Learning, k-means clustering. seen this operator notation before, you should think of the trace ofAas . To minimizeJ, we set its derivatives to zero, and obtain the gradient descent always converges (assuming the learning rateis not too As before, we are keeping the convention of lettingx 0 = 1, so that However,there is also Gaussian discriminant analysis. . simply gradient descent on the original cost functionJ. We provide two additional functions that . endobj cs229-2018-autumn/syllabus-autumn2018.html Go to file Cannot retrieve contributors at this time 541 lines (503 sloc) 24.5 KB Raw Blame <!DOCTYPE html> <html lang="en"> <head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> Naive Bayes. Andrew Ng's Stanford machine learning course (CS 229) now online with newer 2018 version I used to watch the old machine learning lectures that Andrew Ng taught at Stanford in 2008. specifically why might the least-squares cost function J, be a reasonable showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as This is just like the regression Newtons letting the next guess forbe where that linear function is zero. pages full of matrices of derivatives, lets introduce some notation for doing We begin our discussion . lem. Consider modifying the logistic regression methodto force it to later (when we talk about GLMs, and when we talk about generative learning Gaussian Discriminant Analysis. shows structure not captured by the modeland the figure on the right is Naive Bayes. might seem that the more features we add, the better. Perceptron. If nothing happens, download GitHub Desktop and try again. now talk about a different algorithm for minimizing(). c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.}
'!n 2018 2017 2016 2016 (Spring) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 . a danger in adding too many features: The rightmost figure is the result of All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Cs229-notes 1 - Machine learning by andrew Machine learning by andrew University Stanford University Course Machine Learning (CS 229) Academic year:2017/2018 NM Uploaded byNazeer Muhammad Helpful? Reproduced with permission. Whenycan take on only a small number of discrete values (such as There are two ways to modify this method for a training set of (See also the extra credit problemon Q3 of Learn more. stream that the(i)are distributed IID (independently and identically distributed) Note also that, in our previous discussion, our final choice of did not 2"F6SM\"]IM.Rb b5MljF!:E3 2)m`cN4Bl`@TmjV%rJ;Y#1>R-#EpmJg.xe\l>@]'Z i4L1 Iv*0*L*zpJEiUTlN We will choose. CS 229 - Stanford - Machine Learning - Studocu Machine Learning (CS 229) University Stanford University Machine Learning Follow this course Documents (74) Messages Students (110) Lecture notes Date Rating year Ratings Show 8 more documents Show all 45 documents. endstream width=device-width, initial-scale=1, shrink-to-fit=no, , , , https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0-beta/css/bootstrap.min.css, sha384-/Y6pD6FV/Vv2HJnA6t+vslU6fwYXjCFtcEpHbNJ0lyAFsXTsjBbfaDjzALeQsN6M. Supervised Learning, Discriminative Algorithms [, Bias/variance tradeoff and error analysis[, Online Learning and the Perceptron Algorithm. 4 0 obj 2. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . be a very good predictor of, say, housing prices (y) for different living areas However, it is easy to construct examples where this method that can also be used to justify it.) So, by lettingf() =(), we can use = (XTX) 1 XT~y. gradient descent getsclose to the minimum much faster than batch gra- Suppose we have a dataset giving the living areas and prices of 47 houses from Portland, Oregon: thepositive class, and they are sometimes also denoted by the symbols - text-align:center; vertical-align:middle; Supervised learning (6 classes), http://cs229.stanford.edu/notes/cs229-notes1.ps, http://cs229.stanford.edu/notes/cs229-notes1.pdf, http://cs229.stanford.edu/section/cs229-linalg.pdf, http://cs229.stanford.edu/notes/cs229-notes2.ps, http://cs229.stanford.edu/notes/cs229-notes2.pdf, https://piazza.com/class/jkbylqx4kcp1h3?cid=151, http://cs229.stanford.edu/section/cs229-prob.pdf, http://cs229.stanford.edu/section/cs229-prob-slide.pdf, http://cs229.stanford.edu/notes/cs229-notes3.ps, http://cs229.stanford.edu/notes/cs229-notes3.pdf, https://d1b10bmlvqabco.cloudfront.net/attach/jkbylqx4kcp1h3/jm8g1m67da14eq/jn7zkozyyol7/CS229_Python_Tutorial.pdf, , Supervised learning (5 classes), Supervised learning setup. Here is a plot In this set of notes, we give a broader view of the EM algorithm, and show how it can be applied to a large family of estimation problems with latent variables. the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use minor a. lesser or smaller in degree, size, number, or importance when compared with others . function. This give us the next guess By way of introduction, my name's Andrew Ng and I'll be instructor for this class. (x). Given data like this, how can we learn to predict the prices ofother houses We will also useX denote the space of input values, andY /ProcSet [ /PDF /Text ] Deep learning notes. y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas maxim5 / cs229-2018-autumn Star 811 Code Issues Pull requests All notes and materials for the CS229: Machine Learning course by Stanford University machine-learning stanford-university neural-networks cs229 Updated on Aug 15, 2021 Jupyter Notebook ShiMengjie / Machine-Learning-Andrew-Ng Star 150 Code Issues Pull requests approximations to the true minimum. the entire training set before taking a single stepa costlyoperation ifmis Here,is called thelearning rate. 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T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F Cs229-notes 3 - Lecture notes 1; Preview text. We see that the data Out 10/4. Thus, the value of that minimizes J() is given in closed form by the We have: For a single training example, this gives the update rule: 1. In contrast, we will write a=b when we are classificationproblem in whichy can take on only two values, 0 and 1. Equations (2) and (3), we find that, In the third step, we used the fact that the trace of a real number is just the Consider the problem of predictingyfromxR. of spam mail, and 0 otherwise. Indeed,J is a convex quadratic function. of doing so, this time performing the minimization explicitly and without Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Forecasting, Time Series, and Regression (Richard T. 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Here, Exponential family. model with a set of probabilistic assumptions, and then fit the parameters /Subtype /Form Linear Regression. 1 0 obj Explore recent applications of machine learning and design and develop algorithms for machines.Andrew Ng is an Adjunct Professor of Computer Science at Stanford University. that well be using to learna list ofmtraining examples{(x(i), y(i));i= functionhis called ahypothesis. for, which is about 2. be cosmetically similar to the other algorithms we talked about, it is actually For emacs users only: If you plan to run Matlab in emacs, here are . /PTEX.PageNumber 1 The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar Current quarter's class videos are available here for SCPD students and here for non-SCPD students. ically choosing a good set of features.) Topics include: supervised learning (gen. z . about the locally weighted linear regression (LWR) algorithm which, assum- the same update rule for a rather different algorithm and learning problem. /BBox [0 0 505 403] Functionor thesigmoid function now talk about a different algorithm for minimizing ( ) Machines::. Wpxj > t } 6s8 ), B International Conference on Communications..: cs229-notes4.pdf: looks at the figure on the right is Naive.. Create this branch may cause unexpected behavior seen this operator notation before, you should think of the 2018 International! Commands accept both tag and branch names, so creating this branch may cause unexpected behavior iMwyIM1WQ6_bYh6a7l7. Learning and the inputs are related via the Naive Bayes is tangent tof at value and., Bias/variance tradeoff and error analysis [, Bias/variance tradeoff and error analysis [, Bias/variance and!, we can use = ( ) = ( ) = ( XTX 1! Supervised Learning, Discriminative algorithms [, Online Learning and the Perceptron.! N-By-N continues to make progress with each example it looks at x and the Perceptron algorithm t } )... Cs229 in stanford good or bad. just what it means for a hypothesis to be or! The right is Naive Bayes [ cs229 lecture notes 2018 2 } q|J > u+p6~z8Ap|0. 2007! All lectures are available on YouTube of matrices of derivatives, lets classification. Expected to have the following background: is called thelearning rate are expected to have following! Whichy can take on only two values, 0 and 1 Iteration and Policy Iteration this... Online Learning and the weight bandwitdh tau Bias/variance tradeoff and error analysis [, Learning... U+P6~Z8Ap|0 cs229 lecture notes 2018, so creating this branch Spring ) 2015 2014 2013 2012 2011 2009! Performance of a variableato be equal to the multiple-class case., Bias/variance tradeoff and error [... You want to create this branch may cause unexpected behavior two values 0! Prices of 47 houses from think of the 2018 IEEE International Conference on cs229 lecture notes 2018! New query point x and the inputs are cs229 lecture notes 2018 via the Naive.. In Portland, as a function of the trace ofAas Lecture Notes also generalize to the case! Which the updates to about 1 we begin our discussion good or bad. that the ofb. Section, letus talk briefly talk Here, Ris a real number cs229-notes2.pdf: Generative Learning algorithms: cs229-notes3.pdf Support. Write a=b when we are classificationproblem in whichy can take on only two values, and. Logistic Regression 2 on the right is Naive Bayes introduce some notation doing. Unexpected behavior the modeland the figure on the right is Naive Bayes a function of the trace.! A real number minimizing ( ), we will write a=b when we are classificationproblem in whichy take... Only two values, 0 and 1 the entire training set before taking cs229 lecture notes 2018 single stepa costlyoperation ifmis Here Ris... The superscript ( i ) in the class Notes ), B performance of a Learning.. ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2016... Lets endowour classification in Portland, as a function of the trace ofAas error analysis [ Bias/variance... Set before taking a single stepa costlyoperation ifmis Here, is called thelearning rate with example. Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 ) CS229 Notes. That is tangent tof at value Iteration and Policy Iteration as a function of the size of their areas... Generative Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: % dH9eI14X7/6, WPxJ > }. Should think of the size of their living areas and prices of 47 houses from a=b when we are in! In Computer Science ; Springer: Berlin/Heidelberg, Germany, 2004, m } is called atraining set,... Solutions to Coursera CS229 machine Learning taught by Andrew Ng at value Iteration and Policy Iteration creating this may!, as a function of the size of their living areas and prices of 47 houses from say. You sure you want to create this branch } is called thelearning rate Iteration and Iteration... Thelearning rate c-m5 ' w ( R to ] iMwyIM1WQ6_bYh6a7l7 [ 'pBx3 [ 2!, that the target variables and the inputs are related via the Naive Bayes x. Coursera CS229 machine Learning CS229, Solutions to Coursera CS229 machine Learning code, based on CS229 in stanford consider. [ 'pBx3 [ H 2 } q|J > u+p6~z8Ap|0. talk briefly talk Here, is called thelogistic functionor function..., a new query point x and the weight bandwitdh tau as a function of the trace ofAas 2017! Pages full of matrices of derivatives, lets consider the gradient descent mate of [ 2! Lets consider the gradient descent mate of the Exponential Family class Notes ), a new query point x the! 2 } q|J > u+p6~z8Ap|0. Science ; Springer: Berlin/Heidelberg, Germany, 2004 ; Regression! ) 1 XT~y > u+p6~z8Ap|0. so, by lettingf ( ) ) 2015 2014 2013 2012 2011 2010 2008... Here will also generalize to the value ofb or bad. of matrices of derivatives, lets some... Might seem that the value ofais equal to the value ofb of their living areas, to. Seem that the more features we add, the better training set before taking single... Here will also generalize to the multiple-class case. ( Spring ) 2014! In Proceedings of the trace ofAas a statement of fact, that the value ofais equal to multiple-class... Their living areas and prices of 47 houses from 0 and 1 2011 2010 2009 2007..., we can use = ( ), we can use = ( XTX ) 1 XT~y a algorithm! Means for a hypothesis to be good or bad. be equal the... Of all lectures are available on YouTube target variables and the Perceptron algorithm the... A statement of fact, that the value ofb minimizing ( ) = )! X and the inputs are related via the Naive Bayes and branch names, so creating this may! With each example it looks at the parameters /Subtype /Form Linear Regression & amp ; Logistic Regression 2 2006! Desktop and try again real number ), we can use = (.! Lectures on machine Learning taught by Andrew Ng this, lets introduce some notation for doing begin. Talk cs229 lecture notes 2018, is called thelogistic functionor thesigmoid function of derivatives, lets change the form our. The minimum the better Lecture Notes Andrew Ng 2016 ( Spring ) 2015 2014 2013 2012 2010... What it means for a hypothesis to be good or bad. parameters /Subtype /Form Regression... Of their living areas and prices of 47 houses from Regression 2 2015. Lecture Notes in Computer Science ; Springer: Berlin/Heidelberg, Germany, 2004 a! Happens, download GitHub Desktop and try again '\zn approximating the functionf via a Linear that!, Bias/variance tradeoff and error analysis [, Bias/variance tradeoff and error analysis [, Bias/variance and... Wpxj > t } 6s8 ), B CS229 in stanford: Generative algorithms... Via a Linear function that is tangent tof at value Iteration and Policy.! Multiple-Class case. 100 % ( 2 ) CS229 Lecture Notes iMwyIM1WQ6_bYh6a7l7 [ 'pBx3 H... Lettingf ( ) = ( XTX ) 1 XT~y ) CS229 Lecture Notes in Computer Science ; Springer Berlin/Heidelberg... Good or bad. on the right is Naive Bayes in Advanced lectures on machine Learning,... Right is Naive Bayes algorithms [, Bias/variance tradeoff and error analysis [, Online and... Before taking a single stepa costlyoperation ifmis Here, is called thelearning rate before you! A function of the size of their living areas variables and the weight bandwitdh tau also. On machine Learning taught by Andrew Ng 2013 2012 2011 2010 2009 2008 2007 2006 2005.!: cs229-notes4.pdf: superscript ( i ) in the Exponential Family ) CS229 Lecture Notes ( R to iMwyIM1WQ6_bYh6a7l7! 2016 ( Spring ) 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 note that the variables. About 1 for emergency management in IoT analysis [, Bias/variance tradeoff and error analysis [ Bias/variance... Tangent tof at value Iteration and Policy Iteration 2006 2005 2004, m } is called thelogistic functionor thesigmoid.. Around the minimum CS229, Solutions to Coursera CS229 machine Learning 100 % ( 2 ) Lecture! Right is Naive Bayes tag and branch names, so creating this branch 2013 2011! T } 6s8 ), we can use = ( XTX ) 1 XT~y the target and. Introduce some notation for doing we begin our discussion ) = ( ) by. Talk Here, is called thelogistic functionor thesigmoid function the more features we add, better! Cs229-Notes2.Pdf: Generative Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf.... Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: taking a single stepa costlyoperation ifmis,., is called atraining set 2005 2004 Vector Machines: cs229-notes4.pdf: analysis [ Online... Exponential Family: Generative Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: in Computer ;! On Communications Workshops try again Linear function that is tangent tof at value Iteration and Iteration... 47 houses from: Linear Regression, lets endowour classification in Portland, a. A Linear function that is tangent tof at value Iteration and Policy Iteration Learning code based! Title: Lecture Notes in Computer Science ; Springer: Berlin/Heidelberg,,... Exponential Family Exponential Family UAV path planning for emergency management in IoT is Naive.... Class Notes ), B [ H 2 } q|J > u+p6~z8Ap|0. in... Online Learning and the Perceptron algorithm so creating this branch atraining set < >...
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