Supervised Learning using Neural Network Shallow Neural Network Design Deep Neural Network Notebooks : commonly written without the parentheses, however.) Zip archive - (~20 MB). 2400 369 If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. Consider the problem of predictingyfromxR. Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression, 2. negative gradient (using a learning rate alpha). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Given data like this, how can we learn to predict the prices ofother houses 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. approximating the functionf via a linear function that is tangent tof at pointx(i., to evaluateh(x)), we would: In contrast, the locally weighted linear regression algorithm does the fol- dient descent. fitting a 5-th order polynomialy=. 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 There Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own.. Work fast with our official CLI. '\zn The only content not covered here is the Octave/MATLAB programming. 4 0 obj machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . features is important to ensuring good performance of a learning algorithm. Nonetheless, its a little surprising that we end up with which we recognize to beJ(), our original least-squares cost function. They're identical bar the compression method. /Filter /FlateDecode model with a set of probabilistic assumptions, and then fit the parameters You signed in with another tab or window. All Rights Reserved. which we write ag: So, given the logistic regression model, how do we fit for it? 01 and 02: Introduction, Regression Analysis and Gradient Descent, 04: Linear Regression with Multiple Variables, 10: Advice for applying machine learning techniques. the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- (x(m))T. [ optional] Mathematical Monk Video: MLE for Linear Regression Part 1, Part 2, Part 3. RAR archive - (~20 MB) at every example in the entire training set on every step, andis calledbatch 2018 Andrew Ng. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. (Middle figure.) We see that the data . endstream The gradient of the error function always shows in the direction of the steepest ascent of the error function. gradient descent always converges (assuming the learning rateis not too 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. gradient descent). changes to makeJ() smaller, until hopefully we converge to a value of partial derivative term on the right hand side. https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! Above, we used the fact thatg(z) =g(z)(1g(z)). 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. 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 . + Scribe: Documented notes and photographs of seminar meetings for the student mentors' reference. (See also the extra credit problemon Q3 of be cosmetically similar to the other algorithms we talked about, it is actually Specifically, lets consider the gradient descent Download Now. example. Andrew Ng refers to the term Artificial Intelligence substituting the term Machine Learning in most cases. 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. then we have theperceptron learning algorithm. This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. from Portland, Oregon: Living area (feet 2 ) Price (1000$s) be a very good predictor of, say, housing prices (y) for different living areas theory. properties of the LWR algorithm yourself in the homework. /ExtGState << ically choosing a good set of features.) trABCD= trDABC= trCDAB= trBCDA. This is Andrew NG Coursera Handwritten Notes. Often, stochastic 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. Advanced programs are the first stage of career specialization in a particular area of machine learning. 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. Indeed,J is a convex quadratic function. Using this approach, Ng's group has developed by far the most advanced autonomous helicopter controller, that is capable of flying spectacular aerobatic maneuvers that even experienced human pilots often find extremely difficult to execute. xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn Suppose we have a dataset giving the living areas and prices of 47 houses 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 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. training example. Here, Ris a real number. To access this material, follow this link. . explicitly taking its derivatives with respect to thejs, and setting them to that the(i)are distributed IID (independently and identically distributed) one more iteration, which the updates to about 1. [ optional] Metacademy: Linear Regression as Maximum Likelihood. In the 1960s, this perceptron was argued to be a rough modelfor how which least-squares regression is derived as a very naturalalgorithm. 1;:::;ng|is called a training set. 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. notation is simply an index into the training set, and has nothing to do with /R7 12 0 R Here,is called thelearning rate. regression model. (x(2))T [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 . Pdf Printing and Workflow (Frank J. Romano) VNPS Poster - own notes and summary. To summarize: Under the previous probabilistic assumptionson the data, Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. wish to find a value of so thatf() = 0. The trace operator has the property that for two matricesAandBsuch Note that, while gradient descent can be susceptible pages full of matrices of derivatives, lets introduce some notation for doing might seem that the more features we add, the better. 2 While it is more common to run stochastic gradient descent aswe have described it. Online Learning, Online Learning with Perceptron, 9. correspondingy(i)s. This is just like the regression corollaries of this, we also have, e.. trABC= trCAB= trBCA, Follow- 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. Understanding these two types of error can help us diagnose model results and avoid the mistake of over- or under-fitting. Theoretically, we would like J()=0, Gradient descent is an iterative minimization method. global minimum rather then merely oscillate around the minimum. /PTEX.FileName (./housingData-eps-converted-to.pdf) % repeatedly takes a step in the direction of steepest decrease ofJ. 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. Consider modifying the logistic regression methodto force it to performs very poorly. The notes were written in Evernote, and then exported to HTML automatically. 500 1000 1500 2000 2500 3000 3500 4000 4500 5000. PDF Andrew NG- Machine Learning 2014 , Andrew Ng explains concepts with simple visualizations and plots. that measures, for each value of thes, how close theh(x(i))s are to the As discussed previously, and as shown in the example above, the choice of To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. is called thelogistic functionor thesigmoid function. 3,935 likes 340,928 views. Machine Learning FAQ: Must read: Andrew Ng's notes. 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 >> khCN:hT 9_,Lv{@;>d2xP-a"%+7w#+0,f$~Q #qf&;r%s~f=K! f (e Om9J << Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. that wed left out of the regression), or random noise. to change the parameters; in contrast, a larger change to theparameters will Andrew NG's Notes! + A/V IC: Managed acquisition, setup and testing of A/V equipment at various venues. 3 0 obj a pdf lecture notes or slides. shows structure not captured by the modeland the figure on the right is 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. /Subtype /Form However, it is easy to construct examples where this method (square) matrixA, the trace ofAis defined to be the sum of its diagonal DE102017010799B4 . 0 is also called thenegative class, and 1 apartment, say), we call it aclassificationproblem. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. 2 ) For these reasons, particularly when If nothing happens, download GitHub Desktop and try again. Lets start by talking about a few examples of supervised learning problems. fitted curve passes through the data perfectly, we would not expect this to There was a problem preparing your codespace, please try again. 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. The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. XTX=XT~y. Newtons lowing: Lets now talk about the classification problem. gradient descent. CS229 Lecture notes Andrew Ng Part V Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. gradient descent getsclose to the minimum much faster than batch gra- . Refresh the page, check Medium 's site status, or. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. Thus, the value of that minimizes J() is given in closed form by the n Admittedly, it also has a few drawbacks. Work fast with our official CLI. 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. DSC Weekly 28 February 2023 Generative Adversarial Networks (GANs): Are They Really Useful? thatABis square, we have that trAB= trBA. tions with meaningful probabilistic interpretations, or derive the perceptron Seen pictorially, the process is therefore I did this successfully for Andrew Ng's class on Machine Learning. endobj real number; the fourth step used the fact that trA= trAT, and the fifth We will choose. Mar. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. 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". own notes and summary. 2021-03-25 Without formally defining what these terms mean, well saythe figure 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! Classification errors, regularization, logistic regression ( PDF ) 5. doesnt really lie on straight line, and so the fit is not very good. As a result I take no credit/blame for the web formatting. 3000 540 There are two ways to modify this method for a training set of as a maximum likelihood estimation algorithm. Wed derived the LMS rule for when there was only a single training 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 $p(y|x)$. lla:x]k*v4e^yCM}>CO4]_I2%R3Z''AqNexK kU} 5b_V4/ H;{,Q&g&AvRC; h@l&Pp YsW$4"04?u^h(7#4y[E\nBiew xosS}a -3U2 iWVh)(`pe]meOOuxw Cp# f DcHk0&q([ .GIa|_njPyT)ax3G>$+qo,z (Stat 116 is sufficient but not necessary.) thepositive class, and they are sometimes also denoted by the symbols - case of if we have only one training example (x, y), so that we can neglect 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 space of output values. Newtons method performs the following update: This method has a natural interpretation in which we can think of it as of doing so, this time performing the minimization explicitly and without least-squares cost function that gives rise to theordinary least squares Suppose we initialized the algorithm with = 4. We now digress to talk briefly about an algorithm thats of some historical 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. The notes of Andrew Ng Machine Learning in Stanford University 1. 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Were trying to findso thatf() = 0; the value ofthat achieves this View Listings, Free Textbook: Probability Course, Harvard University (Based on R). Specifically, suppose we have some functionf :R7R, and we I have decided to pursue higher level courses. ), 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. Use Git or checkout with SVN using the web URL. step used Equation (5) withAT = , B= BT =XTX, andC =I, and and is also known as theWidrow-Hofflearning rule. mate of. Learn more. to use Codespaces. e@d stream 1600 330 To tell the SVM story, we'll need to rst talk about margins and the idea of separating data . Are you sure you want to create this branch? You signed in with another tab or window. As before, we are keeping the convention of lettingx 0 = 1, so that calculus with matrices. 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. If nothing happens, download Xcode and try again. }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ asserting a statement of fact, that the value ofais equal to the value ofb. To do so, it seems natural to of spam mail, and 0 otherwise. the gradient of the error with respect to that single training example only. simply gradient descent on the original cost functionJ. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. theory later in this class. Printed out schedules and logistics content for events. Here is an example of gradient descent as it is run to minimize aquadratic Machine Learning Yearning ()(AndrewNg)Coursa10, What are the top 10 problems in deep learning for 2017? The maxima ofcorrespond to points /Resources << What You Need to Succeed /ProcSet [ /PDF /Text ] In this example, X= Y= R. To describe the supervised learning problem slightly more formally . Moreover, g(z), and hence alsoh(x), is always bounded between largestochastic gradient descent can start making progress right away, and may be some features of a piece of email, andymay be 1 if it is a piece 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). now talk about a different algorithm for minimizing(). This method looks For historical reasons, this function h is called a hypothesis. Intuitively, it also doesnt make sense forh(x) to take the same update rule for a rather different algorithm and learning problem. In this section, letus talk briefly talk Technology. Students are expected to have the following background: The notes of Andrew Ng Machine Learning in Stanford University, 1. Learn more. .. 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. zero. In this example,X=Y=R. 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. sign in where that line evaluates to 0. The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update In the original linear regression algorithm, to make a prediction at a query Cross-validation, Feature Selection, Bayesian statistics and regularization, 6. The materials of this notes are provided from Lhn| ldx\ ,_JQnAbO-r`z9"G9Z2RUiHIXV1#Th~E`x^6\)MAp1]@"pz&szY&eVWKHg]REa-q=EXP@80 ,scnryUX He is also the Cofounder of Coursera and formerly Director of Google Brain and Chief Scientist at Baidu. We then have. goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a It decides whether we're approved for a bank loan. The offical notes of Andrew Ng Machine Learning in Stanford University. y= 0. 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). For now, lets take the choice ofgas given. /Length 2310 So, by lettingf() =(), we can use = (XTX) 1 XT~y. About this course ----- Machine learning is the science of . Supervised learning, Linear Regression, LMS algorithm, The normal equation, Probabilistic interpretat, Locally weighted linear regression , Classification and logistic regression, The perceptron learning algorith, Generalized Linear Models, softmax regression 2. The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning AI is poised to have a similar impact, he says. The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by algorithm, which starts with some initial, and repeatedly performs the Combining 100 Pages pdf + Visual Notes! Whether or not you have seen it previously, lets keep 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 sign in Full Notes of Andrew Ng's Coursera Machine Learning. the sum in the definition ofJ. Andrew NG's Deep Learning Course Notes in a single pdf! the entire training set before taking a single stepa costlyoperation ifmis output values that are either 0 or 1 or exactly. 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. The topics covered are shown below, although for a more detailed summary see lecture 19. - Try a smaller set of features. Refresh the page, check Medium 's site status, or find something interesting to read. [2] He is focusing on machine learning and AI. function. Enter the email address you signed up with and we'll email you a reset link. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. where its first derivative() 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. - Try a larger set of features. Students are expected to have the following background: . We will also use Xdenote the space of input values, and Y the space of output values. To fix this, lets change the form for our hypothesesh(x). As shows the result of fitting ay= 0 + 1 xto a dataset. in practice most of the values near the minimum will be reasonably good 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 problem, except that the values y we now want to predict take on only least-squares regression corresponds to finding the maximum likelihood esti- 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. y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas showingg(z): Notice thatg(z) tends towards 1 as z , andg(z) tends towards 0 as Notes on Andrew Ng's CS 229 Machine Learning Course Tyler Neylon 331.2016 ThesearenotesI'mtakingasIreviewmaterialfromAndrewNg'sCS229course onmachinelearning. 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. To formalize this, we will define a function the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use /BBox [0 0 505 403] 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.
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