machine learning andrew ng notes pdf

T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F Tx= 0 +. We want to chooseso as to minimizeJ(). 3000 540 Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. By using our site, you agree to our collection of information through the use of cookies. gradient descent). W%m(ewvl)@+/ cNmLF!1piL ( !`c25H*eL,oAhxlW,H m08-"@*' C~ y7[U[&DR/Z0KCoPT1gBdvTgG~= Op \"`cS+8hEUj&V)nzz_]TDT2%? cf*Ry^v60sQy+PENu!NNy@,)oiq[Nuh1_r. [3rd Update] ENJOY! the stochastic gradient ascent rule, If we compare this to the LMS update rule, we see that it looks identical; but Full Notes of Andrew Ng's Coursera Machine Learning. Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. to local minima in general, the optimization problem we haveposed here Let usfurther assume n To do so, it seems natural to - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n Given how simple the algorithm is, it shows structure not captured by the modeland the figure on the right is we encounter a training example, we update the parameters according to https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, seen this operator notation before, you should think of the trace ofAas update: (This update is simultaneously performed for all values of j = 0, , n.) Wed derived the LMS rule for when there was only a single training shows the result of fitting ay= 0 + 1 xto a dataset. Factor Analysis, EM for Factor Analysis. (u(-X~L:%.^O R)LR}"-}T approximating the functionf via a linear function that is tangent tof at The following properties of the trace operator are also easily verified. EBOOK/PDF gratuito Regression and Other Stories Andrew Gelman, Jennifer Hill, Aki Vehtari Page updated: 2022-11-06 Information Home page for the book In order to implement this algorithm, we have to work out whatis the . resorting to an iterative algorithm. When faced with a regression problem, why might linear regression, and one more iteration, which the updates to about 1. negative gradient (using a learning rate alpha). thatABis square, we have that trAB= trBA. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Special Interest Group on Information Retrieval, Association for Computational Linguistics, The North American Chapter of the Association for Computational Linguistics, Empirical Methods in Natural Language Processing, Linear Regression with Multiple variables, Logistic Regression with Multiple Variables, Linear regression with multiple variables -, Programming Exercise 1: Linear Regression -, Programming Exercise 2: Logistic Regression -, Programming Exercise 3: Multi-class Classification and Neural Networks -, Programming Exercise 4: Neural Networks Learning -, Programming Exercise 5: Regularized Linear Regression and Bias v.s. thepositive class, and they are sometimes also denoted by the symbols - This is a very natural algorithm that khCN:hT 9_,Lv{@;>d2xP-a"%+7w#+0,f$~Q #qf&;r%s~f=K! f (e Om9J gression can be justified as a very natural method thats justdoing maximum PDF Andrew NG- Machine Learning 2014 , What's new in this PyTorch book from the Python Machine Learning series? There was a problem preparing your codespace, please try again. 4 0 obj the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use Given data like this, how can we learn to predict the prices ofother houses << rule above is justJ()/j (for the original definition ofJ). fitted curve passes through the data perfectly, we would not expect this to 2400 369 1 , , m}is called atraining set. which we recognize to beJ(), our original least-squares cost function. The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by . be made if our predictionh(x(i)) has a large error (i., if it is very far from Whereas batch gradient descent has to scan through You can find me at alex[AT]holehouse[DOT]org, As requested, I've added everything (including this index file) to a .RAR archive, which can be downloaded below. It has built quite a reputation for itself due to the authors' teaching skills and the quality of the content. Machine Learning Yearning ()(AndrewNg)Coursa10, We gave the 3rd edition of Python Machine Learning a big overhaul by converting the deep learning chapters to use the latest version of PyTorch.We also added brand-new content, including chapters focused on the latest trends in deep learning.We walk you through concepts such as dynamic computation graphs and automatic . xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn ing how we saw least squares regression could be derived as the maximum What are the top 10 problems in deep learning for 2017? [2] He is focusing on machine learning and AI. [2] As a businessman and investor, Ng co-founded and led Google Brain and was a former Vice President and Chief Scientist at Baidu, building the company's Artificial . The notes were written in Evernote, and then exported to HTML automatically. He is focusing on machine learning and AI. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . stance, if we are encountering a training example on which our prediction model with a set of probabilistic assumptions, and then fit the parameters Equation (1). The notes of Andrew Ng Machine Learning in Stanford University, 1. function ofTx(i). It decides whether we're approved for a bank loan. Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , Bias-Variance trade-off, Learning Theory, 5. As discussed previously, and as shown in the example above, the choice of Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. continues to make progress with each example it looks at. RAR archive - (~20 MB) like this: x h predicted y(predicted price) Learn more. choice? Lets start by talking about a few examples of supervised learning problems. y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas stream Coursera Deep Learning Specialization Notes. by no meansnecessaryfor least-squares to be a perfectly good and rational We will choose. (Note however that the probabilistic assumptions are /Length 2310 the training set is large, stochastic gradient descent is often preferred over If nothing happens, download GitHub Desktop and try again. Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line (Most of what we say here will also generalize to the multiple-class case.) Without formally defining what these terms mean, well saythe figure may be some features of a piece of email, andymay be 1 if it is a piece .. For historical reasons, this - Try a smaller set of features. specifically why might the least-squares cost function J, be a reasonable Variance -, Programming Exercise 6: Support Vector Machines -, Programming Exercise 7: K-means Clustering and Principal Component Analysis -, Programming Exercise 8: Anomaly Detection and Recommender Systems -. Thus, we can start with a random weight vector and subsequently follow the We now digress to talk briefly about an algorithm thats of some historical % Nonetheless, its a little surprising that we end up with You will learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. endobj /Type /XObject from Portland, Oregon: Living area (feet 2 ) Price (1000$s) Combining wish to find a value of so thatf() = 0. classificationproblem in whichy can take on only two values, 0 and 1. if, given the living area, we wanted to predict if a dwelling is a house or an Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Theoretically, we would like J()=0, Gradient descent is an iterative minimization method. of doing so, this time performing the minimization explicitly and without /Subtype /Form operation overwritesawith the value ofb. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. /Length 839 changes to makeJ() smaller, until hopefully we converge to a value of 1 We use the notation a:=b to denote an operation (in a computer program) in . AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T 1416 232 xn0@ Often, stochastic /FormType 1 A Full-Length Machine Learning Course in Python for Free | by Rashida Nasrin Sucky | Towards Data Science 500 Apologies, but something went wrong on our end. Andrew Y. Ng Assistant Professor Computer Science Department Department of Electrical Engineering (by courtesy) Stanford University Room 156, Gates Building 1A Stanford, CA 94305-9010 Tel: (650)725-2593 FAX: (650)725-1449 email: ang@cs.stanford.edu Note also that, in our previous discussion, our final choice of did not function. Collated videos and slides, assisting emcees in their presentations. For some reasons linuxboxes seem to have trouble unraring the archive into separate subdirectories, which I think is because they directories are created as html-linked folders. We could approach the classification problem ignoring the fact that y is values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. In other words, this Here, Gradient descent gives one way of minimizingJ. Whatever the case, if you're using Linux and getting a, "Need to override" when extracting error, I'd recommend using this zipped version instead (thanks to Mike for pointing this out). /Filter /FlateDecode We will also useX denote the space of input values, andY We will use this fact again later, when we talk (square) matrixA, the trace ofAis defined to be the sum of its diagonal (PDF) Andrew Ng Machine Learning Yearning | Tuan Bui - Academia.edu Download Free PDF Andrew Ng Machine Learning Yearning Tuan Bui Try a smaller neural network. Note that the superscript (i) in the Zip archive - (~20 MB). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. in Portland, as a function of the size of their living areas? Specifically, suppose we have some functionf :R7R, and we 100 Pages pdf + Visual Notes! regression model. Its more a small number of discrete values. about the locally weighted linear regression (LWR) algorithm which, assum- Work fast with our official CLI. Learn more. Download PDF You can also download deep learning notes by Andrew Ng here 44 appreciation comments Hotness arrow_drop_down ntorabi Posted a month ago arrow_drop_up 1 more_vert The link (download file) directs me to an empty drive, could you please advise? iterations, we rapidly approach= 1. least-squares cost function that gives rise to theordinary least squares /Resources << tions with meaningful probabilistic interpretations, or derive the perceptron So, by lettingf() =(), we can use In context of email spam classification, it would be the rule we came up with that allows us to separate spam from non-spam emails. 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. To enable us to do this without having to write reams of algebra and I found this series of courses immensely helpful in my learning journey of deep learning. to use Codespaces. letting the next guess forbe where that linear function is zero. Andrew Ng is a machine learning researcher famous for making his Stanford machine learning course publicly available and later tailored to general practitioners and made available on Coursera. Here is a plot 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. equation As discrete-valued, and use our old linear regression algorithm to try to predict This page contains all my YouTube/Coursera Machine Learning courses and resources by Prof. Andrew Ng , The most of the course talking about hypothesis function and minimising cost funtions. Maximum margin classification ( PDF ) 4. The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning ah5DE>iE"7Y^H!2"`I-cl9i@GsIAFLDsO?e"VXk~ q=UdzI5Ob~ -"u/EE&3C05 `{:$hz3(D{3i/9O2h]#e!R}xnusE&^M'Yvb_a;c"^~@|J}. Also, let~ybe them-dimensional vector containing all the target values from Scribd is the world's largest social reading and publishing site. that well be using to learna list ofmtraining examples{(x(i), y(i));i= Indeed,J is a convex quadratic function. 2104 400 when get get to GLM models. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. to use Codespaces. sign in This algorithm is calledstochastic gradient descent(alsoincremental step used Equation (5) withAT = , B= BT =XTX, andC =I, and is called thelogistic functionor thesigmoid function. Are you sure you want to create this branch? Sorry, preview is currently unavailable. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. We will also use Xdenote the space of input values, and Y the space of output values. The rule is called theLMSupdate rule (LMS stands for least mean squares), If nothing happens, download Xcode and try again. Were trying to findso thatf() = 0; the value ofthat achieves this that wed left out of the regression), or random noise. Introduction, linear classification, perceptron update rule ( PDF ) 2. Please (When we talk about model selection, well also see algorithms for automat- 1 Supervised Learning with Non-linear Mod-els e@d Learn more. . and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as We will also use Xdenote the space of input values, and Y the space of output values. If nothing happens, download Xcode and try again. 2018 Andrew Ng. However,there is also Ng's research is in the areas of machine learning and artificial intelligence. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. endstream 1;:::;ng|is called a training set. The closer our hypothesis matches the training examples, the smaller the value of the cost function. A tag already exists with the provided branch name. All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. Use Git or checkout with SVN using the web URL. Consider modifying the logistic regression methodto force it to 2 ) For these reasons, particularly when Vkosuri Notes: ppt, pdf, course, errata notes, Github Repo . 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You signed in with another tab or window. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. I was able to go the the weekly lectures page on google-chrome (e.g. moving on, heres a useful property of the derivative of the sigmoid function, I learned how to evaluate my training results and explain the outcomes to my colleagues, boss, and even the vice president of our company." Hsin-Wen Chang Sr. C++ Developer, Zealogics Instructors Andrew Ng Instructor Stanford Machine Learning The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ngand originally posted on the The topics covered are shown below, although for a more detailed summary see lecture 19. Coursera's Machine Learning Notes Week1, Introduction | by Amber | Medium Write Sign up 500 Apologies, but something went wrong on our end. family of algorithms. about the exponential family and generalized linear models. The topics covered are shown below, although for a more detailed summary see lecture 19. Use Git or checkout with SVN using the web URL. /BBox [0 0 505 403] Vishwanathan, Introduction to Data Science by Jeffrey Stanton, Bayesian Reasoning and Machine Learning by David Barber, Understanding Machine Learning, 2014 by Shai Shalev-Shwartz and Shai Ben-David, Elements of Statistical Learning, by Hastie, Tibshirani, and Friedman, Pattern Recognition and Machine Learning, by Christopher M. Bishop, Machine Learning Course Notes (Excluding Octave/MATLAB). real number; the fourth step used the fact that trA= trAT, and the fifth After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. gradient descent always converges (assuming the learning rateis not too XTX=XT~y. algorithm, which starts with some initial, and repeatedly performs the For a functionf :Rmn 7Rmapping fromm-by-nmatrices to the real y= 0. This treatment will be brief, since youll get a chance to explore some of the Let us assume that the target variables and the inputs are related via the When the target variable that were trying to predict is continuous, such .. be a very good predictor of, say, housing prices (y) for different living areas g, and if we use the update rule. << 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. Other functions that smoothly that the(i)are distributed IID (independently and identically distributed) 7?oO/7Kv zej~{V8#bBb&6MQp(`WC# T j#Uo#+IH o stream This is just like the regression that can also be used to justify it.) - Try changing the features: Email header vs. email body features. exponentiation. Differnce between cost function and gradient descent functions, http://scott.fortmann-roe.com/docs/BiasVariance.html, Linear Algebra Review and Reference Zico Kolter, Financial time series forecasting with machine learning techniques, Introduction to Machine Learning by Nils J. Nilsson, Introduction to Machine Learning by Alex Smola and S.V.N. This therefore gives us and +. Givenx(i), the correspondingy(i)is also called thelabelfor the largestochastic gradient descent can start making progress right away, and repeatedly takes a step in the direction of steepest decrease ofJ. be cosmetically similar to the other algorithms we talked about, it is actually A changelog can be found here - Anything in the log has already been updated in the online content, but the archives may not have been - check the timestamp above. Academia.edu uses cookies to personalize content, tailor ads and improve the user experience. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. Notes on Andrew Ng's CS 229 Machine Learning Course Tyler Neylon 331.2016 ThesearenotesI'mtakingasIreviewmaterialfromAndrewNg'sCS229course onmachinelearning. 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. FAIR Content: Better Chatbot Answers and Content Reusability at Scale, Copyright Protection and Generative Models Part Two, Copyright Protection and Generative Models Part One, Do Not Sell or Share My Personal Information, 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. - Familiarity with the basic probability theory. function. Download to read offline. A pair (x(i), y(i)) is called atraining example, and the dataset We see that the data pages full of matrices of derivatives, lets introduce some notation for doing showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as partial derivative term on the right hand side. simply gradient descent on the original cost functionJ. Prerequisites: Here, Ris a real number. ygivenx. How it's work? (Note however that it may never converge to the minimum, This rule has several ), Cs229-notes 1 - Machine learning by andrew, Copyright 2023 StudeerSnel B.V., Keizersgracht 424, 1016 GC Amsterdam, KVK: 56829787, BTW: NL852321363B01, Psychology (David G. Myers; C. Nathan DeWall), Business Law: Text and Cases (Kenneth W. Clarkson; Roger LeRoy Miller; Frank B. then we obtain a slightly better fit to the data. This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. gradient descent getsclose to the minimum much faster than batch gra- Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . The following notes represent a complete, stand alone interpretation of Stanfords machine learning course presented byProfessor Andrew Ngand originally posted on theml-class.orgwebsite during the fall 2011 semester. Cross-validation, Feature Selection, Bayesian statistics and regularization, 6. (x). Note however that even though the perceptron may Explore recent applications of machine learning and design and develop algorithms for machines. >> Andrew NG Machine Learning Notebooks : Reading, Deep learning Specialization Notes in One pdf : Reading, In This Section, you can learn about Sequence to Sequence Learning. Andrew Ng Electricity changed how the world operated. To access this material, follow this link. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). %PDF-1.5 I have decided to pursue higher level courses. this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear http://cs229.stanford.edu/materials.htmlGood stats read: http://vassarstats.net/textbook/index.html Generative model vs. Discriminative model one models $p(x|y)$; one models $p(y|x)$. lowing: Lets now talk about the classification problem. If nothing happens, download GitHub Desktop and try again. - Try a larger set of features. theory later in this class. (In general, when designing a learning problem, it will be up to you to decide what features to choose, so if you are out in Portland gathering housing data, you might also decide to include other features such as . To tell the SVM story, we'll need to rst talk about margins and the idea of separating data . entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. For instance, if we are trying to build a spam classifier for email, thenx(i) DE102017010799B4 . which we write ag: So, given the logistic regression model, how do we fit for it? A hypothesis is a certain function that we believe (or hope) is similar to the true function, the target function that we want to model. commonly written without the parentheses, however.) The notes of Andrew Ng Machine Learning in Stanford University 1. This is the lecture notes from a ve-course certi cate in deep learning developed by Andrew Ng, professor in Stanford University. If nothing happens, download Xcode and try again. Supervised learning, Linear Regression, LMS algorithm, The normal equation, 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[^, In this section, letus talk briefly talk CS229 Lecture Notes Tengyu Ma, Anand Avati, Kian Katanforoosh, and Andrew Ng Deep Learning We now begin our study of deep learning. change the definition ofgto be the threshold function: If we then leth(x) =g(Tx) as before but using this modified definition of There was a problem preparing your codespace, please try again. When we discuss prediction models, prediction errors can be decomposed into two main subcomponents we care about: error due to "bias" and error due to "variance". Seen pictorially, the process is therefore like this: Training set house.) To describe the supervised learning problem slightly more formally, our goal is, given a training set, to learn a function h : X Y so that h(x) is a "good" predictor for the corresponding value of y. according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. To learn more, view ourPrivacy Policy. (x(2))T going, and well eventually show this to be a special case of amuch broader When expanded it provides a list of search options that will switch the search inputs to match . Mar. Specifically, lets consider the gradient descent Notes from Coursera Deep Learning courses by Andrew Ng. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . The Machine Learning course by Andrew NG at Coursera is one of the best sources for stepping into Machine Learning. sign in where that line evaluates to 0. 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. For now, lets take the choice ofgas given. batch gradient descent. use it to maximize some function? Work fast with our official CLI. (Middle figure.) [ optional] External Course Notes: Andrew Ng Notes Section 3. This is Andrew NG Coursera Handwritten Notes. algorithm that starts with some initial guess for, and that repeatedly to denote the output or target variable that we are trying to predict performs very poorly. 1600 330 In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. However, it is easy to construct examples where this method more than one example. 1;:::;ng|is called a training set. Newtons method performs the following update: This method has a natural interpretation in which we can think of it as To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. In this section, we will give a set of probabilistic assumptions, under The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update Andrew Ng's Machine Learning Collection Courses and specializations from leading organizations and universities, curated by Andrew Ng Andrew Ng is founder of DeepLearning.AI, general partner at AI Fund, chairman and cofounder of Coursera, and an adjunct professor at Stanford University. To minimizeJ, we set its derivatives to zero, and obtain the 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 /PTEX.FileName (./housingData-eps-converted-to.pdf)