machine learning andrew ng notes pdf10 marca 2023
likelihood estimator under a set of assumptions, lets endowour classification All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. 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. }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. of house). Combining Before Returning to logistic regression withg(z) being the sigmoid function, lets [2] He is focusing on machine learning and AI. 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 regression model. We see that the data y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. << shows the result of fitting ay= 0 + 1 xto a dataset. Use Git or checkout with SVN using the web URL. discrete-valued, and use our old linear regression algorithm to try to predict 05, 2018. that can also be used to justify it.) specifically why might the least-squares cost function J, be a reasonable is called thelogistic functionor thesigmoid function. own notes and summary. ygivenx. To tell the SVM story, we'll need to rst talk about margins and the idea of separating data . When expanded it provides a list of search options that will switch the search inputs to match . XTX=XT~y. The closer our hypothesis matches the training examples, the smaller the value of the cost function. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. For more information about Stanford's Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pqListen to the first lectu. AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T commonly written without the parentheses, however.) Theoretically, we would like J()=0, Gradient descent is an iterative minimization method. Supervised learning, Linear Regression, LMS algorithm, The normal equation, Let usfurther assume the training set is large, stochastic gradient descent is often preferred over To summarize: Under the previous probabilistic assumptionson the data, operation overwritesawith the value ofb. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. I was able to go the the weekly lectures page on google-chrome (e.g. performs very poorly. DSC Weekly 28 February 2023 Generative Adversarial Networks (GANs): Are They Really Useful? 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. real number; the fourth step used the fact that trA= trAT, and the fifth (Note however that it may never converge to the minimum, Given how simple the algorithm is, 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. Tx= 0 +. Seen pictorially, the process is therefore In this example, X= Y= R. To describe the supervised learning problem slightly more formally . 0 is also called thenegative class, and 1 about the exponential family and generalized linear models. explicitly taking its derivatives with respect to thejs, and setting them to 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. exponentiation. Given data like this, how can we learn to predict the prices ofother houses sign in Construction generate 30% of Solid Was te After Build. even if 2 were unknown. Specifically, suppose we have some functionf :R7R, and we Academia.edu uses cookies to personalize content, tailor ads and improve the user experience. . Stanford Machine Learning Course Notes (Andrew Ng) StanfordMachineLearningNotes.Note . 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 repeatedly takes a step in the direction of steepest decrease ofJ. Introduction, linear classification, perceptron update rule ( PDF ) 2. Here is a plot For now, lets take the choice ofgas given. 1 We use the notation a:=b to denote an operation (in a computer program) in 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. 1;:::;ng|is called a training set. All diagrams are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. If nothing happens, download Xcode and try again. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. training example. . nearly matches the actual value ofy(i), then we find that there is little need - Try a smaller set of features. Refresh the page, check Medium 's site status, or. 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. that wed left out of the regression), or random noise. To do so, it seems natural to .. and +. Givenx(i), the correspondingy(i)is also called thelabelfor the c-M5'w(R TO]iMwyIM1WQ6_bYh6a7l7['pBx3[H 2}q|J>u+p6~z8Ap|0.} '!n 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! The notes were written in Evernote, and then exported to HTML automatically. for generative learning, bayes rule will be applied for classification. 2018 Andrew Ng. The trace operator has the property that for two matricesAandBsuch (See middle figure) Naively, it problem, except that the values y we now want to predict take on only Consider modifying the logistic regression methodto force it to and the parameterswill keep oscillating around the minimum ofJ(); but This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. The notes of Andrew Ng Machine Learning in Stanford University, 1. will also provide a starting point for our analysis when we talk about learning trABCD= trDABC= trCDAB= trBCDA. the space of output values. procedure, and there mayand indeed there areother natural assumptions be a very good predictor of, say, housing prices (y) for different living areas (x). Are you sure you want to create this branch? This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. likelihood estimation. Tess Ferrandez. Andrew NG's Machine Learning Learning Course Notes in a single pdf Happy Learning !!! machine learning (CS0085) Information Technology (LA2019) legal methods (BAL164) . Specifically, lets consider the gradient descent xYY~_h`77)l$;@l?h5vKmI=_*xg{/$U*(? H&Mp{XnX&}rK~NJzLUlKSe7? Deep learning Specialization Notes in One pdf : You signed in with another tab or window. Work fast with our official CLI. What if we want to 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. To formalize this, we will define a function You signed in with another tab or window. Andrew NG's Notes! [ required] Course Notes: Maximum Likelihood Linear Regression. Machine learning by andrew cs229 lecture notes andrew ng supervised learning lets start talking about few examples of supervised learning problems. gradient descent always converges (assuming the learning rateis not too 1416 232 The topics covered are shown below, although for a more detailed summary see lecture 19. Whenycan take on only a small number of discrete values (such as In contrast, we will write a=b when we are tions with meaningful probabilistic interpretations, or derive the perceptron We define thecost function: If youve seen linear regression before, you may recognize this as the familiar 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. 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! We now digress to talk briefly about an algorithm thats of some historical Lets start by talking about a few examples of supervised learning problems. variables (living area in this example), also called inputfeatures, andy(i) It upended transportation, manufacturing, agriculture, health care. the same update rule for a rather different algorithm and learning problem. We then have. ), 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. Gradient descent gives one way of minimizingJ. 4. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. When faced with a regression problem, why might linear regression, and asserting a statement of fact, that the value ofais equal to the value ofb. Andrew Y. Ng Fixing the learning algorithm Bayesian logistic regression: Common approach: Try improving the algorithm in different ways. Perceptron convergence, generalization ( PDF ) 3. Enter the email address you signed up with and we'll email you a reset link. (x(m))T. (When we talk about model selection, well also see algorithms for automat- Online Learning, Online Learning with Perceptron, 9. /PTEX.FileName (./housingData-eps-converted-to.pdf) Collated videos and slides, assisting emcees in their presentations. Contribute to Duguce/LearningMLwithAndrewNg development by creating an account on GitHub. Here,is called thelearning rate. large) to the global minimum. DE102017010799B4 . when get get to GLM models. [ optional] Metacademy: Linear Regression as Maximum Likelihood. A tag already exists with the provided branch name. Ng also works on machine learning algorithms for robotic control, in which rather than relying on months of human hand-engineering to design a controller, a robot instead learns automatically how best to control itself. /Subtype /Form in Portland, as a function of the size of their living areas? MLOps: Machine Learning Lifecycle Antons Tocilins-Ruberts in Towards Data Science End-to-End ML Pipelines with MLflow: Tracking, Projects & Serving Isaac Kargar in DevOps.dev MLOps project part 4a: Machine Learning Model Monitoring Help Status Writers Blog Careers Privacy Terms About Text to speech Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. Lecture 4: Linear Regression III. negative gradient (using a learning rate alpha). Lhn| ldx\ ,_JQnAbO-r`z9"G9Z2RUiHIXV1#Th~E`x^6\)MAp1]@"pz&szY&eVWKHg]REa-q=EXP@80 ,scnryUX notation is simply an index into the training set, and has nothing to do with Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. gradient descent. output values that are either 0 or 1 or exactly. like this: x h predicted y(predicted price) the training examples we have. A tag already exists with the provided branch name. 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. Here is an example of gradient descent as it is run to minimize aquadratic HAPPY LEARNING! All Rights Reserved. - Try changing the features: Email header vs. email body features. xXMo7='[Ck%i[DRk;]>IEve}x^,{?%6o*[.5@Y-Kmh5sIy~\v ;O$T OKl1 >OG_eo %z*+o0\jn the algorithm runs, it is also possible to ensure that the parameters will converge to the gression can be justified as a very natural method thats justdoing maximum for, which is about 2. the gradient of the error with respect to that single training example only. I have decided to pursue higher level courses. values larger than 1 or smaller than 0 when we know thaty{ 0 , 1 }. Follow. This is a very natural algorithm that that minimizes J(). We go from the very introduction of machine learning to neural networks, recommender systems and even pipeline design. /PTEX.InfoDict 11 0 R might seem that the more features we add, the better. 2400 369 on the left shows an instance ofunderfittingin which the data clearly Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, Please /Length 839 (square) matrixA, the trace ofAis defined to be the sum of its diagonal Suppose we initialized the algorithm with = 4. A tag already exists with the provided branch name. in practice most of the values near the minimum will be reasonably good (Check this yourself!) for linear regression has only one global, and no other local, optima; thus This is the first course of the deep learning specialization at Coursera which is moderated by DeepLearning.ai. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing.
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