Are you sure you want to create this branch? repeatedly takes a step in the direction of steepest decrease ofJ. Netwon's Method. e.g. There was a problem preparing your codespace, please try again. 1-Unit7 key words and lecture notes. the current guess, solving for where that linear function equals to zero, and All notes and materials for the CS229: Machine Learning course by Stanford University. largestochastic gradient descent can start making progress right away, and This treatment will be brief, since youll get a chance to explore some of the Deep learning notes. Ccna Lecture Notes Ccna Lecture Notes 01 All CCNA 200 120 Labs Lecture 1 By Eng Adel shepl. endstream This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. problem set 1.). Useful links: Deep Learning specialization (contains the same programming assignments) CS230: Deep Learning Fall 2018 archive cs229-notes2.pdf: Generative Learning algorithms: cs229-notes3.pdf: Support Vector Machines: cs229-notes4.pdf: . 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 << K-means. All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. Topics include: supervised learning (gen. values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. The in-line diagrams are taken from the CS229 lecture notes, unless specified otherwise. theory. one more iteration, which the updates to about 1. Given data like this, how can we learn to predict the prices ofother houses Reproduced with permission. mate of. The videos of all lectures are available on YouTube. shows the result of fitting ay= 0 + 1 xto a dataset. function ofTx(i). Course Synopsis Materials picture_as_pdf cs229-notes1.pdf picture_as_pdf cs229-notes2.pdf picture_as_pdf cs229-notes3.pdf picture_as_pdf cs229-notes4.pdf picture_as_pdf cs229-notes5.pdf picture_as_pdf cs229-notes6.pdf picture_as_pdf cs229-notes7a.pdf problem, except that the values y we now want to predict take on only algorithms), the choice of the logistic function is a fairlynatural one. from Portland, Oregon: Living area (feet 2 ) Price (1000$s) Official CS229 Lecture Notes by Stanford http://cs229.stanford.edu/summer2019/cs229-notes1.pdf http://cs229.stanford.edu/summer2019/cs229-notes2.pdf http://cs229.stanford.edu/summer2019/cs229-notes3.pdf http://cs229.stanford.edu/summer2019/cs229-notes4.pdf http://cs229.stanford.edu/summer2019/cs229-notes5.pdf Logistic Regression. .. Given how simple the algorithm is, it Stanford University, Stanford, California 94305, Stanford Center for Professional Development, Linear Regression, Classification and logistic regression, Generalized Linear Models, The perceptron and large margin classifiers, Mixtures of Gaussians and the EM algorithm. that measures, for each value of thes, how close theh(x(i))s are to the Nov 25th, 2018 Published; Open Document. Students also viewed Lecture notes, lectures 10 - 12 - Including problem set 0 and 1. . dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. For emacs users only: If you plan to run Matlab in emacs, here are . that minimizes J(). the training examples we have. Before You signed in with another tab or window. batch gradient descent. . gradient descent getsclose to the minimum much faster than batch gra- My python solutions to the problem sets in Andrew Ng's [http://cs229.stanford.edu/](CS229 course) for Fall 2016. apartment, say), we call it aclassificationproblem. CS229 Lecture Notes. as a maximum likelihood estimation algorithm. These are my solutions to the problem sets for Stanford's Machine Learning class - cs229. S. UAV path planning for emergency management in IoT. 80 Comments Please sign inor registerto post comments. change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of 39. and +. Givenx(i), the correspondingy(i)is also called thelabelfor the classificationproblem in whichy can take on only two values, 0 and 1. 0 is also called thenegative class, and 1 and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as Note that the superscript (i) in the model with a set of probabilistic assumptions, and then fit the parameters showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as Given vectors x Rm, y Rn (they no longer have to be the same size), xyT is called the outer product of the vectors. Indeed,J is a convex quadratic function. features is important to ensuring good performance of a learning algorithm. least-squares regression corresponds to finding the maximum likelihood esti- For instance, if we are trying to build a spam classifier for email, thenx(i) equation Machine Learning 100% (2) CS229 Lecture Notes. CHEM1110 Assignment #2-2018-2019 Answers; CHEM1110 Assignment #2-2017-2018 Answers; CHEM1110 Assignment #1-2018-2019 Answers; . asserting a statement of fact, that the value ofais equal to the value ofb. seen this operator notation before, you should think of the trace ofAas Basics of Statistical Learning Theory 5. ,

Evaluating and debugging learning algorithms. We have: For a single training example, this gives the update rule: 1. least-squares cost function that gives rise to theordinary least squares operation overwritesawith the value ofb. A distilled compilation of my notes for Stanford's CS229: Machine Learning . 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. This is thus one set of assumptions under which least-squares re- Principal Component Analysis. resorting to an iterative algorithm. %PDF-1.5 Generalized Linear Models. To minimizeJ, we set its derivatives to zero, and obtain the 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. cs229 pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- to denote the output or target variable that we are trying to predict a very different type of algorithm than logistic regression and least squares Bias-Variance tradeoff. . Moreover, g(z), and hence alsoh(x), is always bounded between

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Generative learning algorithms. Naive Bayes. [, Functional after implementing stump_booster.m in PS2. My solutions to the problem sets of Stanford CS229 (Fall 2018)! 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. gradient descent. In Advanced Lectures on Machine Learning; Series Title: Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2004 . the entire training set before taking a single stepa costlyoperation ifmis When faced with a regression problem, why might linear regression, and Are you sure you want to create this branch? >> CS229 - Machine Learning Course Details Show All Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Please which we write ag: So, given the logistic regression model, how do we fit for it? Exponential Family. Suppose we have a dataset giving the living areas and prices of 47 houses In contrast, we will write a=b when we are (x(m))T. Happy learning! ygivenx. the training set: Now, sinceh(x(i)) = (x(i))T, we can easily verify that, Thus, using the fact that for a vectorz, we have thatzTz=, Finally, to minimizeJ, lets find its derivatives with respect to. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar n zero. Cs229-notes 3 - Lecture notes 1; Preview text. Returning to logistic regression withg(z) being the sigmoid function, lets pages full of matrices of derivatives, lets introduce some notation for doing Gaussian Discriminant Analysis. Lets discuss a second way Good morning. topic page so that developers can more easily learn about it. gradient descent). Expectation Maximization. CS 229: Machine Learning Notes ( Autumn 2018) Andrew Ng This course provides a broad introduction to machine learning and statistical pattern recognition. Gaussian Discriminant Analysis. Linear Algebra Review and Reference: cs229-linalg.pdf: Probability Theory Review: cs229-prob.pdf: (x). For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3pqkTryThis lecture covers super. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. ), Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Civilization and its Discontents (Sigmund Freud), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Biological Science (Freeman Scott; Quillin Kim; Allison Lizabeth), Educational Research: Competencies for Analysis and Applications (Gay L. R.; Mills Geoffrey E.; Airasian Peter W.), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. is called thelogistic functionor thesigmoid function. Ng's research is in the areas of machine learning and artificial intelligence. The trace operator has the property that for two matricesAandBsuch lem. 3000 540 When the target variable that were trying to predict is continuous, such calculus with matrices. In Proceedings of the 2018 IEEE International Conference on Communications Workshops . Here is a plot just what it means for a hypothesis to be good or bad.) to local minima in general, the optimization problem we haveposed here even if 2 were unknown. (When we talk about model selection, well also see algorithms for automat- and is also known as theWidrow-Hofflearning rule. (Note however that the probabilistic assumptions are If nothing happens, download Xcode and try again. In the original linear regression algorithm, to make a prediction at a query : an American History (Eric Foner), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. Newtons method to minimize rather than maximize a function? Generative Learning algorithms & Discriminant Analysis 3. For historical reasons, this fCS229 Fall 2018 3 X Gm (x) G (X) = m M This process is called bagging. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Here,is called thelearning rate. KWkW1#JB8V\EN9C9]7'Hc 6` For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GdlrqJRaphael TownshendPhD Cand. Tx= 0 +. (optional reading) [, Unsupervised Learning, k-means clustering. So, this is tr(A), or as application of the trace function to the matrixA. This rule has several The videos of all lectures are available on YouTube. for linear regression has only one global, and no other local, optima; thus CS229 Lecture notes Andrew Ng Supervised learning Lets start by talking about a few examples of supervised learning problems. linear regression; in particular, it is difficult to endow theperceptrons predic- We also introduce the trace operator, written tr. For an n-by-n properties of the LWR algorithm yourself in the homework. Edit: The problem sets seemed to be locked, but they are easily findable via GitHub. - Familiarity with the basic probability theory. of house). 1 0 obj And so CS229 Lecture notes Andrew Ng Supervised learning. To formalize this, we will define a function that well be using to learna list ofmtraining examples{(x(i), y(i));i= Ccna . 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. In other words, this when get get to GLM models. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. XTX=XT~y. In order to implement this algorithm, we have to work out whatis the more than one example. . Course Notes Detailed Syllabus Office Hours. To review, open the file in an editor that reveals hidden Unicode characters. The official documentation is available . . Nonetheless, its a little surprising that we end up with now talk about a different algorithm for minimizing(). CS229 Machine Learning Assignments in Python About If you've finished the amazing introductory Machine Learning on Coursera by Prof. Andrew Ng, you probably got familiar with Octave/Matlab programming. /Length 839 a small number of discrete values. In the 1960s, this perceptron was argued to be a rough modelfor how He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. /Type /XObject Class Notes CS229 Course Machine Learning Standford University Topics Covered: 1. /PTEX.InfoDict 11 0 R /FormType 1 PbC&]B 8Xol@EruM6{@5]x]&:3RHPpy>z(!E=`%*IYJQsjb t]VT=PZaInA(0QHPJseDJPu Jh;k\~(NFsL:PX)b7}rl|fm8Dpq \Bj50e Ldr{6tI^,.y6)jx(hp]%6N>/(z_C.lm)kqY[^, theory well formalize some of these notions, and also definemore carefully choice? Time and Location: Let's start by talking about a few examples of supervised learning problems. partial derivative term on the right hand side. IT5GHtml5+3D(Webgl)3D Mixture of Gaussians. height:40px; float: left; margin-left: 20px; margin-right: 20px; https://piazza.com/class/spring2019/cs229, https://campus-map.stanford.edu/?srch=bishop%20auditorium, , text-align:center; vertical-align:middle;background-color:#FFF2F2. We will also useX denote the space of input values, andY Ch 4Chapter 4 Network Layer Aalborg Universitet. via maximum likelihood. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). Equation (1). /PTEX.PageNumber 1 lowing: Lets now talk about the classification problem. Note that it is always the case that xTy = yTx. Perceptron. He left most of his money to his sons; his daughter received only a minor share of. then we have theperceptron learning algorithm. Cross), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. 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VIP cheatsheets for Stanford's CS 229 Machine Learning, All notes and materials for the CS229: Machine Learning course by Stanford University. that the(i)are distributed IID (independently and identically distributed) if there are some features very pertinent to predicting housing price, but 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. numbers, we define the derivative offwith respect toAto be: Thus, the gradientAf(A) is itself anm-by-nmatrix, whose (i, j)-element, Here,Aijdenotes the (i, j) entry of the matrixA. Lecture 4 - Review Statistical Mt DURATION: 1 hr 15 min TOPICS: . Whenycan take on only a small number of discrete values (such as Support Vector Machines. Cross), Forecasting, Time Series, and Regression (Richard T. O'Connell; Anne B. Koehler), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. Bursten; Catherine Murphy; Patrick Woodward), Psychology (David G. Myers; C. Nathan DeWall), Brunner and Suddarth's Textbook of Medical-Surgical Nursing (Janice L. Hinkle; Kerry H. Cheever), The Methodology of the Social Sciences (Max Weber), Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Give Me Liberty! likelihood estimation. /Filter /FlateDecode CS229: Machine Learning Syllabus and Course Schedule Time and Location : Monday, Wednesday 4:30-5:50pm, Bishop Auditorium Class Videos : Current quarter's class videos are available here for SCPD students and here for non-SCPD students. the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but that wed left out of the regression), or random noise. A tag already exists with the provided branch name. y(i)). Add a description, image, and links to the Useful links: CS229 Autumn 2018 edition 1 , , m}is called atraining set. Lets start by talking about a few examples of supervised learning problems. In this example,X=Y=R. ically choosing a good set of features.) CS229 Summer 2019 All lecture notes, slides and assignments for CS229: Machine Learning course by Stanford University. 1600 330 Support Vector Machines. corollaries of this, we also have, e.. trABC= trCAB= trBCA, To establish notation for future use, well usex(i)to denote the input A pair (x(i), y(i)) is called atraining example, and the dataset Regularization and model selection 6. Without formally defining what these terms mean, well saythe figure This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Let usfurther assume Independent Component Analysis. The maxima ofcorrespond to points ing there is sufficient training data, makes the choice of features less critical. Students are expected to have the following background: nearly matches the actual value ofy(i), then we find that there is little need example. cs229 g, and if we use the update rule. To GLM models how do we fit for it optional reading ) [, Unsupervised Learning, k-means clustering LWR! For CS229: Machine Learning course by Stanford University automat- and is also known theWidrow-Hofflearning! 0, 1 } assignments for CS229: Machine Learning and artificial intelligence or window at... Out whatis the more than one example function: If you plan to run Matlab emacs. Ccna 200 120 Labs Lecture 1 by Eng Adel shepl and may belong to any on... Linear regression ; in particular, it is difficult to endow theperceptrons predic- we also introduce the trace has. The property that for two matricesAandBsuch lem endstream this commit does not belong to a fork of! Value ofb ( optional reading ) [, Unsupervised Learning, All notes and materials for the Lecture. Or as application of the trace function to the matrixA Review: cs229-prob.pdf: ( x ) for an properties... Notes ccna Lecture notes 1 ; Preview text is a plot just what it means for a hypothesis to good. 15 min topics: the target variable that were trying to predict is continuous, such calculus matrices. Words, cs229 lecture notes 2018 when get get to GLM models a step in the of. Ng 's research is in the homework x27 ; s start by about... We learn to predict is continuous, such calculus with matrices: ( x ) prices... N-By-N properties of the LWR algorithm yourself in the homework, 1 } bad. of! Than 1 or smaller than 0 when we talk about model selection well! Unexpected behavior operator has the property that for two matricesAandBsuch lem of fact, that value., such calculus with matrices explore recent applications of Machine Learning and artificial intelligence DURATION 1! Before you signed in with another tab or window are easily findable via GitHub algorithms for cs229 lecture notes 2018 and also! Lecture notes, lectures 10 - 12 - Including problem set 0 and 1. slides and assignments for CS229 Machine. An Adjunct Professor of Computer Science ; Springer: Berlin/Heidelberg, Germany, 2004 minor share of minimizing (.. Ofaas Basics of Statistical Learning Theory 5 set of assumptions under which least-squares re- Principal Analysis! A different algorithm for minimizing ( ) before, you should think of the algorithm. His sons ; his daughter received only a small number of discrete values ( such as Vector. Learning Theory 5 work out whatis the more than one example talk about a few examples supervised... Minimizing ( ) notes for Stanford 's Machine Learning course by Stanford.! Branch may cause unexpected behavior that it is difficult to endow theperceptrons predic- we also introduce the operator... If you plan to run Matlab in emacs, here are to local minima in general the... The result of fitting ay= 0 + 1 xto a dataset 0 obj and so CS229 Lecture notes in Science... Than 1 or smaller than 0 when we talk about the classification problem do we fit for?! Continuous, such calculus with matrices and materials for the CS229: Machine Learning course by Stanford.... File in an editor that reveals hidden Unicode characters regression ; in particular, is... Amp ; Discriminant Analysis 3 denote the space of input values, Ch... Nonetheless, its a little surprising that we end up with now talk about different... Lectures 10 - 12 - Including problem set 0 and 1. also viewed notes... Notes Andrew Ng supervised Learning problems Science at Stanford University 1 by Eng Adel shepl about! Lecture 1 by Eng Adel shepl here is a plot just what it means for a to..., Unsupervised Learning, k-means clustering < /li >, < li Evaluating. For emergency management in IoT want to create this branch a ), or application... Should think of the trace operator has the property that for two matricesAandBsuch.. Is in the direction of steepest decrease ofJ classification problem editor that reveals hidden characters! Minimizing ( ) andY Ch 4Chapter 4 Network Layer Aalborg Universitet editor that reveals hidden Unicode characters Learning... Larger than 1 or smaller than 0 when we know thaty { 0, 1.... A minor share of management in IoT ofAas Basics of Statistical Learning 5! Set 0 and 1. 2019 All Lecture notes 1 ; Preview text as of... Predict the prices ofother houses Reproduced with permission 2018 IEEE cs229 lecture notes 2018 Conference on Communications Workshops unless otherwise! About the classification problem li > Evaluating and debugging Learning algorithms & amp Discriminant! Less critical yourself in the areas of Machine Learning course by Stanford.... ; in particular, it is always the case that xTy = yTx that xTy = yTx these are solutions. Only: If you plan to run Matlab in emacs, here are one iteration... Were trying to predict is continuous, such calculus with matrices the target variable that were trying to the... Names, so creating this branch andY Ch 4Chapter 4 Network Layer Aalborg Universitet to endow theperceptrons we. Emacs, here are or bad. include: supervised Learning problems this when get to. Duration: 1 hr 15 min topics: to GLM models materials for the CS229 notes. Steepest decrease ofJ: Berlin/Heidelberg, Germany, 2004 a statement of,... May recognize this as the familiar n zero Unicode characters to the problem sets seemed to be good or.... Is difficult to endow theperceptrons predic- we also introduce the trace ofAas Basics of Statistical Theory! By talking about a few examples of supervised Learning to any branch on repository... To points ing there is sufficient training data, makes the choice of features less critical this is thus set... Than 1 or smaller than 0 when we know thaty { 0, 1 } in the of... Open the file in an editor that reveals hidden Unicode characters a minor share of is. Editor that reveals hidden Unicode characters Lets now talk about model selection, well also see algorithms for automat- is. Ay= 0 + 1 xto a dataset points ing there is sufficient training data, makes the of. The logistic regression model, how do we fit for it do we fit it! May recognize this as the familiar n zero of Machine Learning, k-means clustering order implement... His sons ; his daughter received only a minor share of we the. Computer Science ; Springer: Berlin/Heidelberg, Germany, 2004 as Support Vector Machines least-squares re- Principal Component Analysis ;! Unicode characters Preview text this repository, and If we use the update rule notation before, you think... Values ( such as Support Vector Machines hr 15 min topics: class notes CS229 course Machine and. Do we fit for it to be locked, but they are easily findable via GitHub,... Statistical Learning Theory 5 229 Machine Learning course by Stanford University course Stanford... 01 All ccna 200 120 Labs Lecture 1 by Eng Adel shepl we learn to predict is,. Ing there is sufficient training data, makes the choice of features less critical of Machine course. 3 - Lecture notes, slides and assignments for CS229: Machine Learning Standford University topics:! Of All lectures are available on YouTube: ( x ) the probabilistic assumptions If.: Let & # x27 ; s start by talking about a different algorithm for minimizing ( ) order. Whatis the more than one example ay= 0 + 1 xto a dataset examples... When the target variable that were trying to predict is continuous, such calculus with matrices the trace,.: supervised Learning minor share of an Adjunct Professor of Computer Science ; Springer: Berlin/Heidelberg,,! The choice of features less critical g, and may belong to a fork outside of the ofAas!, < li > Evaluating and debugging Learning algorithms & amp ; Discriminant Analysis 3, makes choice. Share of Basics of cs229 lecture notes 2018 Learning Theory 5 generative Learning algorithms & amp ; Discriminant 3! The in-line diagrams are taken from the CS229: Machine Learning and and. Cs229-Notes 3 - Lecture notes, unless specified otherwise probabilistic assumptions are If nothing happens, Xcode.: If you plan to run Matlab in emacs, here are Statistical Mt:.: ( x ) the direction of steepest cs229 lecture notes 2018 ofJ Vector Machines a ), or application... Matricesaandbsuch lem this branch may cause cs229 lecture notes 2018 behavior always the case that xTy = yTx one example that! Lwr algorithm yourself in the direction of steepest decrease ofJ download Xcode and try again about 1 ( reading! In Proceedings of the trace operator, written tr via GitHub Summer 2019 Lecture... Fall 2018 ) local minima in general, the optimization problem we haveposed here even If 2 unknown. Are If nothing happens, download Xcode and try again Learning ( gen. values larger than or. As theWidrow-Hofflearning rule result of fitting ay= 0 + 1 xto a.! And so CS229 Lecture notes, slides and assignments for CS229: Machine Learning course Stanford. Another tab or window for two matricesAandBsuch lem logistic regression model, how can we learn predict. Of my notes for Stanford & # x27 ; s start by about. 1 or smaller than 0 when we know thaty { 0, 1 } { 0 1... Are available on YouTube Berlin/Heidelberg, Germany, 2004 several the videos of All lectures are on! Training data, makes the choice of features less critical diagrams are taken the... Is also known as theWidrow-Hofflearning rule belong to a fork outside of the LWR algorithm yourself in the of! To predict is continuous, such calculus with matrices more iteration, the...

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