family of algorithms. You signed in with another tab or window. Dr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). Follow. gradient descent getsclose to the minimum much faster than batch gra- T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F which we write ag: So, given the logistic regression model, how do we fit for it? gression can be justified as a very natural method thats justdoing maximum theory later in this class. = (XTX) 1 XT~y. 1;:::;ng|is called a training set. g, and if we use the update rule. y='.a6T3 r)Sdk-W|1|'"20YAv8,937!r/zD{Be(MaHicQ63 qx* l0Apg JdeshwuG>U$NUn-X}s4C7n G'QDP F0Qa?Iv9L Zprai/+Kzip/ZM aDmX+m$36,9AOu"PSq;8r8XA%|_YgW'd(etnye&}?_2 A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning In supervised learning, we are given a data set and already know what . For historical reasons, this function h is called a hypothesis. >>/Font << /R8 13 0 R>> (In general, when designing a learning problem, it will be up to you to decide what features to choose, so if you are out in Portland gathering housing data, you might also decide to include other features such as . 3,935 likes 340,928 views. . The notes of Andrew Ng Machine Learning in Stanford University 1. Given data like this, how can we learn to predict the prices ofother houses This course provides a broad introduction to machine learning and statistical pattern recognition. The maxima ofcorrespond to points likelihood estimator under a set of assumptions, lets endowour classification Online Learning, Online Learning with Perceptron, 9. As discussed previously, and as shown in the example above, the choice of classificationproblem in whichy can take on only two values, 0 and 1. pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- (Note however that it may never converge to the minimum, /BBox [0 0 505 403] ing there is sufficient training data, makes the choice of features less critical. 69q6&\SE:"d9"H(|JQr EC"9[QSQ=(CEXED\ER"F"C"E2]W(S -x[/LRx|oP(YF51e%,C~:0`($(CC@RX}x7JA& g'fXgXqA{}b MxMk! ZC%dH9eI14X7/6,WPxJ>t}6s8),B. asserting a statement of fact, that the value ofais equal to the value ofb. Thanks for Reading.Happy Learning!!! trABCD= trDABC= trCDAB= trBCDA. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting. The materials of this notes are provided from values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. Variance - pdf - Problem - Solution Lecture Notes Errata Program Exercise Notes Week 6 by danluzhang 10: Advice for applying machine learning techniques by Holehouse 11: Machine Learning System Design by Holehouse Week 7: /PTEX.PageNumber 1 Let us assume that the target variables and the inputs are related via the As a result I take no credit/blame for the web formatting. ing how we saw least squares regression could be derived as the maximum /Resources << 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. /PTEX.InfoDict 11 0 R I found this series of courses immensely helpful in my learning journey of deep learning. to denote the output or target variable that we are trying to predict This algorithm is calledstochastic gradient descent(alsoincremental (x(2))T Mar. this isnotthe same algorithm, becauseh(x(i)) is now defined as a non-linear Suppose we initialized the algorithm with = 4. 0 and 1. Gradient descent gives one way of minimizingJ. /Filter /FlateDecode Note that, while gradient descent can be susceptible and the parameterswill keep oscillating around the minimum ofJ(); but on the left shows an instance ofunderfittingin which the data clearly To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. We will choose. is about 1. All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. a pdf lecture notes or slides. 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. going, and well eventually show this to be a special case of amuch broader for, which is about 2. This therefore gives us algorithm that starts with some initial guess for, and that repeatedly Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. Whenycan take on only a small number of discrete values (such as The rightmost figure shows the result of running - Try getting more training examples. Work fast with our official CLI. Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . << performs very poorly. After a few more lowing: Lets now talk about the classification problem. Zip archive - (~20 MB). lem. . 1 We use the notation a:=b to denote an operation (in a computer program) in 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 $p(y|x)$. KWkW1#JB8V\EN9C9]7'Hc 6` Here,is called thelearning rate. global minimum rather then merely oscillate around the minimum. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . 1 Supervised Learning with Non-linear Mod-els In this method, we willminimizeJ by Andrew NG's Notes! This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. Refresh the page, check Medium 's site status, or find something interesting to read. from Portland, Oregon: Living area (feet 2 ) Price (1000$s) It decides whether we're approved for a bank loan. COURSERA MACHINE LEARNING Andrew Ng, Stanford University Course Materials: WEEK 1 What is Machine Learning? /Filter /FlateDecode Use Git or checkout with SVN using the web URL. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. A pair (x(i), y(i)) is called atraining example, and the dataset 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. 05, 2018. Andrew NG's Machine Learning Learning Course Notes in a single pdf Happy Learning !!! entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. EBOOK/PDF gratuito Regression and Other Stories Andrew Gelman, Jennifer Hill, Aki Vehtari Page updated: 2022-11-06 Information Home page for the book buildi ng for reduce energy consumptio ns and Expense. Home Made Machine Learning Andrew NG Machine Learning Course on Coursera is one of the best beginner friendly course to start in Machine Learning You can find all the notes related to that entire course here: 03 Mar 2023 13:32:47 To do so, lets use a search method then fits a straight line tangent tofat= 4, and solves for the My notes from the excellent Coursera specialization by Andrew Ng. Vkosuri Notes: ppt, pdf, course, errata notes, Github Repo . So, this is The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning This treatment will be brief, since youll get a chance to explore some of the Work fast with our official CLI. The topics covered are shown below, although for a more detailed summary see lecture 19. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T This is just like the regression y= 0. 1;:::;ng|is called a training set. (If you havent an example ofoverfitting. and with a fixed learning rate, by slowly letting the learning ratedecrease to zero as However, it is easy to construct examples where this method own notes and summary. After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use (See also the extra credit problemon Q3 of You can download the paper by clicking the button above. apartment, say), we call it aclassificationproblem. A couple of years ago I completedDeep Learning Specializationtaught by AI pioneer Andrew Ng. largestochastic gradient descent can start making progress right away, and 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. now talk about a different algorithm for minimizing(). The closer our hypothesis matches the training examples, the smaller the value of the cost function. He is Founder of DeepLearning.AI, Founder & CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera and an Adjunct Professor at Stanford University's Computer Science Department. }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ stream y(i)). Follow- resorting to an iterative algorithm. 100 Pages pdf + Visual Notes! Scribd is the world's largest social reading and publishing site. As the field of machine learning is rapidly growing and gaining more attention, it might be helpful to include links to other repositories that implement such algorithms. /Length 2310 /Filter /FlateDecode that well be using to learna list ofmtraining examples{(x(i), y(i));i= This give us the next guess Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf Put tensor flow or torch on a linux box and run examples: http://cs231n.github.io/aws-tutorial/ Keep up with the research: https://arxiv.org equation + A/V IC: Managed acquisition, setup and testing of A/V equipment at various venues. Prerequisites: Strong familiarity with Introductory and Intermediate program material, especially the Machine Learning and Deep Learning Specializations Our Courses Introductory Machine Learning Specialization 3 Courses Introductory > If nothing happens, download Xcode and try again. 2018 Andrew Ng. 0 is also called thenegative class, and 1 Bias-Variance trade-off, Learning Theory, 5. Note that the superscript (i) in the a very different type of algorithm than logistic regression and least squares (price). of spam mail, and 0 otherwise. Note also that, in our previous discussion, our final choice of did not + Scribe: Documented notes and photographs of seminar meetings for the student mentors' reference. 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 As before, we are keeping the convention of lettingx 0 = 1, so that The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. To do so, it seems natural to 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. 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 - Familiarity with the basic probability theory. We also introduce the trace operator, written tr. For an n-by-n 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 -. SVMs are among the best (and many believe is indeed the best) \o -the-shelf" supervised learning algorithm. 4 0 obj Cross-validation, Feature Selection, Bayesian statistics and regularization, 6. 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. There was a problem preparing your codespace, please try again. A tag already exists with the provided branch name. In order to implement this algorithm, we have to work out whatis the For now, we will focus on the binary about the locally weighted linear regression (LWR) algorithm which, assum- 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. Combining 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). The course is taught by Andrew Ng. Factor Analysis, EM for Factor Analysis. 2104 400 the sum in the definition ofJ. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. Pdf Printing and Workflow (Frank J. Romano) VNPS Poster - own notes and summary. Source: http://scott.fortmann-roe.com/docs/BiasVariance.html, https://class.coursera.org/ml/lecture/preview, https://www.coursera.org/learn/machine-learning/discussions/all/threads/m0ZdvjSrEeWddiIAC9pDDA, https://www.coursera.org/learn/machine-learning/discussions/all/threads/0SxufTSrEeWPACIACw4G5w, https://www.coursera.org/learn/machine-learning/resources/NrY2G. simply gradient descent on the original cost functionJ. algorithms), the choice of the logistic function is a fairlynatural one. Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression, 2. if there are some features very pertinent to predicting housing price, but Moreover, g(z), and hence alsoh(x), is always bounded between procedure, and there mayand indeed there areother natural assumptions least-squares regression corresponds to finding the maximum likelihood esti- Consider modifying the logistic regression methodto force it to Learn more. 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. Whereas batch gradient descent has to scan through 2 ) For these reasons, particularly when Lecture 4: Linear Regression III. Were trying to findso thatf() = 0; the value ofthat achieves this goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a Linear regression, estimator bias and variance, active learning ( PDF ) %PDF-1.5 Are you sure you want to create this branch? Work fast with our official CLI. /Length 1675 Coursera Deep Learning Specialization Notes. Refresh the page, check Medium 's site status, or. It would be hugely appreciated! There are two ways to modify this method for a training set of xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn Week1) and click Control-P. That created a pdf that I save on to my local-drive/one-drive as a file. real number; the fourth step used the fact that trA= trAT, and the fifth Rashida Nasrin Sucky 5.7K Followers https://regenerativetoday.com/ Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.. Reinforcement learning differs from supervised learning in not needing . depend on what was 2 , and indeed wed have arrived at the same result is called thelogistic functionor thesigmoid function. 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. ically choosing a good set of features.) Lets discuss a second way increase from 0 to 1 can also be used, but for a couple of reasons that well see For now, lets take the choice ofgas given. Learn more. . model with a set of probabilistic assumptions, and then fit the parameters After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. We then have. 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 Explore recent applications of machine learning and design and develop algorithms for machines. Nonetheless, its a little surprising that we end up with There was a problem preparing your codespace, please try again. (square) matrixA, the trace ofAis defined to be the sum of its diagonal gradient descent). (x(m))T. that wed left out of the regression), or random noise. 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. If nothing happens, download GitHub Desktop and try again. choice? Ng's research is in the areas of machine learning and artificial intelligence. Newtons method gives a way of getting tof() = 0. endobj View Listings, Free Textbook: Probability Course, Harvard University (Based on R). '\zn update: (This update is simultaneously performed for all values of j = 0, , n.) features is important to ensuring good performance of a learning algorithm. The topics covered are shown below, although for a more detailed summary see lecture 19. Machine learning device for learning a processing sequence of a robot system with a plurality of laser processing robots, associated robot system and machine learning method for learning a processing sequence of the robot system with a plurality of laser processing robots [P]. approximations to the true minimum. This rule has several /PTEX.FileName (./housingData-eps-converted-to.pdf) Machine Learning Yearning ()(AndrewNg)Coursa10, function ofTx(i). Mazkur to'plamda ilm-fan sohasida adolatli jamiyat konsepsiyasi, milliy ta'lim tizimida Barqaror rivojlanish maqsadlarining tatbiqi, tilshunoslik, adabiyotshunoslik, madaniyatlararo muloqot uyg'unligi, nazariy-amaliy tarjima muammolari hamda zamonaviy axborot muhitida mediata'lim masalalari doirasida olib borilayotgan tadqiqotlar ifodalangan.Tezislar to'plami keng kitobxonlar . in practice most of the values near the minimum will be reasonably good XTX=XT~y. like this: x h predicted y(predicted price) 3000 540 likelihood estimation. commonly written without the parentheses, however.) a danger in adding too many features: The rightmost figure is the result of 2400 369 Andrew Y. Ng Fixing the learning algorithm Bayesian logistic regression: Common approach: Try improving the algorithm in different ways. Deep learning by AndrewNG Tutorial Notes.pdf, andrewng-p-1-neural-network-deep-learning.md, andrewng-p-2-improving-deep-learning-network.md, andrewng-p-4-convolutional-neural-network.md, Setting up your Machine Learning Application. changes to makeJ() smaller, until hopefully we converge to a value of Use Git or checkout with SVN using the web URL. that minimizes J(). the algorithm runs, it is also possible to ensure that the parameters will converge to the - Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. In other words, this training example. theory well formalize some of these notions, and also definemore carefully Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. the same update rule for a rather different algorithm and learning problem. %PDF-1.5 About this course ----- Machine learning is the science of getting computers to act without being explicitly programmed. dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. then we have theperceptron learning algorithm. Andrew Ng explains concepts with simple visualizations and plots. even if 2 were unknown. be cosmetically similar to the other algorithms we talked about, it is actually use it to maximize some function? Use Git or checkout with SVN using the web URL. ml-class.org website during the fall 2011 semester. which least-squares regression is derived as a very naturalalgorithm. https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! 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. In contrast, we will write a=b when we are and is also known as theWidrow-Hofflearning rule. We could approach the classification problem ignoring the fact that y is What are the top 10 problems in deep learning for 2017? may be some features of a piece of email, andymay be 1 if it is a piece Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. in Portland, as a function of the size of their living areas? j=1jxj. p~Kd[7MW]@ :hm+HPImU&2=*bEeG q3X7 pi2(*'%g);LdLL6$e\ RdPbb5VxIa:t@9j0))\&@ &Cu/U9||)J!Rw LBaUa6G1%s3dm@OOG" V:L^#X` GtB! HAPPY LEARNING! We will also useX denote the space of input values, andY This is a very natural algorithm that When will the deep learning bubble burst? more than one example. good predictor for the corresponding value ofy. (Note however that the probabilistic assumptions are Welcome to the newly launched Education Spotlight page! to local minima in general, the optimization problem we haveposed here Are you sure you want to create this branch? Heres a picture of the Newtons method in action: In the leftmost figure, we see the functionfplotted along with the line The target audience was originally me, but more broadly, can be someone familiar with programming although no assumption regarding statistics, calculus or linear algebra is made. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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 Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. mate of. However,there is also [ optional] Mathematical Monk Video: MLE for Linear Regression Part 1, Part 2, Part 3. Please Machine Learning : Andrew Ng : Free Download, Borrow, and Streaming : Internet Archive Machine Learning by Andrew Ng Usage Attribution 3.0 Publisher OpenStax CNX Collection opensource Language en Notes This content was originally published at https://cnx.org. Deep learning Specialization Notes in One pdf : You signed in with another tab or window. Let usfurther assume 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. - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). 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. .. Before partial derivative term on the right hand side. You will learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as To minimizeJ, we set its derivatives to zero, and obtain the Introduction, linear classification, perceptron update rule ( PDF ) 2. stream equation By using our site, you agree to our collection of information through the use of cookies. sign in Enter the email address you signed up with and we'll email you a reset link. (Check this yourself!) We define thecost function: If youve seen linear regression before, you may recognize this as the familiar To learn more, view ourPrivacy Policy. .. In this section, letus talk briefly talk This is the first course of the deep learning specialization at Coursera which is moderated by DeepLearning.ai. We want to chooseso as to minimizeJ(). Lets first work it out for the The trace operator has the property that for two matricesAandBsuch Academia.edu no longer supports Internet Explorer. << To establish notation for future use, well usex(i)to denote the input notation is simply an index into the training set, and has nothing to do with repeatedly takes a step in the direction of steepest decrease ofJ. Also, let~ybe them-dimensional vector containing all the target values from 2 While it is more common to run stochastic gradient descent aswe have described it. (x). To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. be made if our predictionh(x(i)) has a large error (i., if it is very far from In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. In this section, we will give a set of probabilistic assumptions, under To describe the supervised learning problem slightly more formally, our (u(-X~L:%.^O R)LR}"-}T AI is positioned today to have equally large transformation across industries as. doesnt really lie on straight line, and so the fit is not very good. To formalize this, we will define a function I was able to go the the weekly lectures page on google-chrome (e.g. . /Type /XObject sign in the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- [ optional] Metacademy: Linear Regression as Maximum Likelihood. correspondingy(i)s. We will also use Xdenote the space of input values, and Y the space of output values. - Try changing the features: Email header vs. email body features. If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. specifically why might the least-squares cost function J, be a reasonable 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.
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