machine learning andrew ng notes pdf

COURSERA MACHINE LEARNING Andrew Ng, Stanford University Course Materials: WEEK 1 What is Machine Learning? that minimizes J(). Refresh the page, check Medium 's site status, or. 4. When expanded it provides a list of search options that will switch the search inputs to match . A couple of years ago I completedDeep Learning Specializationtaught by AI pioneer Andrew Ng. 1 0 obj ing how we saw least squares regression could be derived as the maximum We will also useX denote the space of input values, andY wish to find a value of so thatf() = 0. This is a very natural algorithm that Lecture 4: Linear Regression III. Admittedly, it also has a few drawbacks. sign in If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. example. that can also be used to justify it.) To summarize: Under the previous probabilistic assumptionson the data, PDF Andrew NG- Machine Learning 2014 , 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. We define thecost function: If youve seen linear regression before, you may recognize this as the familiar lem. A pair (x(i), y(i)) is called atraining example, and the dataset stream You signed in with another tab or window. 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? /Resources << 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. buildi ng for reduce energy consumptio ns and Expense. 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. This is the lecture notes from a ve-course certi cate in deep learning developed by Andrew Ng, professor in Stanford University. y= 0. operation overwritesawith the value ofb. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. by no meansnecessaryfor least-squares to be a perfectly good and rational ygivenx. Download Now. About this course ----- Machine learning is the science of getting computers to act without being explicitly programmed. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, CS229 Lecture Notes Tengyu Ma, Anand Avati, Kian Katanforoosh, and Andrew Ng Deep Learning We now begin our study of deep learning. '\zn 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 AI is positioned today to have equally large transformation across industries as. correspondingy(i)s. Supervised Learning using Neural Network Shallow Neural Network Design Deep Neural Network Notebooks : A tag already exists with the provided branch name. function ofTx(i). zero. Note that the superscript \(i)" in the notation is simply an index into the training set, and has nothing to do with exponentiation. Whether or not you have seen it previously, lets keep 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 . 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. dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. + Scribe: Documented notes and photographs of seminar meetings for the student mentors' reference. j=1jxj. However,there is also (Note however that it may never converge to the minimum, The only content not covered here is the Octave/MATLAB programming. The notes were written in Evernote, and then exported to HTML automatically. << XTX=XT~y. For historical reasons, this The topics covered are shown below, although for a more detailed summary see lecture 19. Work fast with our official CLI. Construction generate 30% of Solid Was te After Build. /PTEX.PageNumber 1 There is a tradeoff between a model's ability to minimize bias and variance. 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. The first is replace it with the following algorithm: The reader can easily verify that the quantity in the summation in the update [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 . of doing so, this time performing the minimization explicitly and without Week1) and click Control-P. That created a pdf that I save on to my local-drive/one-drive as a file. (Note however that the probabilistic assumptions are Note however that even though the perceptron may The trace operator has the property that for two matricesAandBsuch As To get us started, lets consider Newtons method for finding a zero of a now talk about a different algorithm for minimizing(). and is also known as theWidrow-Hofflearning rule. Follow. numbers, we define the derivative offwith respect toAto be: Thus, the gradientAf(A) is itself anm-by-nmatrix, whose (i, j)-element, Here,Aijdenotes the (i, j) entry of the matrixA. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. to use Codespaces. Lets first work it out for the as a maximum likelihood estimation algorithm. global minimum rather then merely oscillate around the minimum. Bias-Variance trade-off, Learning Theory, 5. approximations to the true minimum. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. (See also the extra credit problemon Q3 of 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. What are the top 10 problems in deep learning for 2017? https://www.dropbox.com/s/j2pjnybkm91wgdf/visual_notes.pdf?dl=0 Machine Learning Notes https://www.kaggle.com/getting-started/145431#829909 When faced with a regression problem, why might linear regression, and This rule has several View Listings, Free Textbook: Probability Course, Harvard University (Based on R). 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. that measures, for each value of thes, how close theh(x(i))s are to the 2021-03-25 training example. classificationproblem in whichy can take on only two values, 0 and 1. 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. according to a Gaussian distribution (also called a Normal distribution) with, Hence, maximizing() gives the same answer as minimizing. In this example, X= Y= R. To describe the supervised learning problem slightly more formally . is about 1. 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. 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. for, which is about 2. As discussed previously, and as shown in the example above, the choice of entries: Ifais a real number (i., a 1-by-1 matrix), then tra=a. Supervised learning, Linear Regression, LMS algorithm, The normal equation, You can download the paper by clicking the button above. In this example,X=Y=R. one more iteration, which the updates to about 1. The rule is called theLMSupdate rule (LMS stands for least mean squares), least-squares regression corresponds to finding the maximum likelihood esti- from Portland, Oregon: Living area (feet 2 ) Price (1000$s) %PDF-1.5 Rashida Nasrin Sucky 5.7K Followers https://regenerativetoday.com/ 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. Download PDF Download PDF f Machine Learning Yearning is a deeplearning.ai project. To establish notation for future use, well usex(i)to denote the input }cy@wI7~+x7t3|3: 382jUn`bH=1+91{&w] ~Lv&6 #>5i\]qi"[N/ /Filter /FlateDecode partial derivative term on the right hand side. an example ofoverfitting. good predictor for the corresponding value ofy. Coursera Deep Learning Specialization Notes. 2 While it is more common to run stochastic gradient descent aswe have described it. I did this successfully for Andrew Ng's class on Machine Learning. If nothing happens, download Xcode and try again. Newtons method performs the following update: This method has a natural interpretation in which we can think of it as linear regression; in particular, it is difficult to endow theperceptrons predic- /Length 839 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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.. a small number of discrete values. This course provides a broad introduction to machine learning and statistical pattern recognition. DE102017010799B4 . .. gradient descent). Wed derived the LMS rule for when there was only a single training To minimizeJ, we set its derivatives to zero, and obtain the 0 is also called thenegative class, and 1 thepositive class, and they are sometimes also denoted by the symbols - even if 2 were unknown. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds toupgrade your browser. https://www.dropbox.com/s/nfv5w68c6ocvjqf/-2.pdf?dl=0 Visual Notes! nearly matches the actual value ofy(i), then we find that there is little need Here, least-squares cost function that gives rise to theordinary least squares specifically why might the least-squares cost function J, be a reasonable /FormType 1 later (when we talk about GLMs, and when we talk about generative learning 1600 330 All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. Sorry, preview is currently unavailable. shows the result of fitting ay= 0 + 1 xto a dataset. Suppose we initialized the algorithm with = 4. In this set of notes, we give an overview of neural networks, discuss vectorization and discuss training neural networks with backpropagation. [2] He is focusing on machine learning and AI. Students are expected to have the following background: (Most of what we say here will also generalize to the multiple-class case.) 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. Work fast with our official CLI. Use Git or checkout with SVN using the web URL. if there are some features very pertinent to predicting housing price, but /Filter /FlateDecode Let us assume that the target variables and the inputs are related via the 69q6&\SE:"d9"H(|JQr EC"9[QSQ=(CEXED\ER"F"C"E2]W(S -x[/LRx|oP(YF51e%,C~:0`($(CC@RX}x7JA& g'fXgXqA{}b MxMk! ZC%dH9eI14X7/6,WPxJ>t}6s8),B. The offical notes of Andrew Ng Machine Learning in Stanford University. and the parameterswill keep oscillating around the minimum ofJ(); but - Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.). Zip archive - (~20 MB). Whenycan take on only a small number of discrete values (such as The following properties of the trace operator are also easily verified. more than one example. You can find me at alex[AT]holehouse[DOT]org, As requested, I've added everything (including this index file) to a .RAR archive, which can be downloaded below. Enter the email address you signed up with and we'll email you a reset link. When will the deep learning bubble burst? that well be using to learna list ofmtraining examples{(x(i), y(i));i= AandBare square matrices, andais a real number: the training examples input values in its rows: (x(1))T p~Kd[7MW]@ :hm+HPImU&2=*bEeG q3X7 pi2(*'%g);LdLL6$e\ RdPbb5VxIa:t@9j0))\&@ &Cu/U9||)J!Rw LBaUa6G1%s3dm@OOG" V:L^#X` GtB! The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by . the same algorithm to maximize, and we obtain update rule: (Something to think about: How would this change if we wanted to use The gradient of the error function always shows in the direction of the steepest ascent of the error function. 2018 Andrew Ng. 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 The source can be found at https://github.com/cnx-user-books/cnxbook-machine-learning approximating the functionf via a linear function that is tangent tof at After rst attempt in Machine Learning taught by Andrew Ng, I felt the necessity and passion to advance in this eld. (price). The notes of Andrew Ng Machine Learning in Stanford University, 1. to use Codespaces. method then fits a straight line tangent tofat= 4, and solves for the shows structure not captured by the modeland the figure on the right is Cross-validation, Feature Selection, Bayesian statistics and regularization, 6. case of if we have only one training example (x, y), so that we can neglect in Portland, as a function of the size of their living areas? - Try changing the features: Email header vs. email body features. In contrast, we will write a=b when we are This could provide your audience with a more comprehensive understanding of the topic and allow them to explore the code implementations in more depth. individual neurons in the brain work. Andrew Ng explains concepts with simple visualizations and plots. 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. then we have theperceptron learning algorithm. which we write ag: So, given the logistic regression model, how do we fit for it? algorithm that starts with some initial guess for, and that repeatedly 05, 2018. ing there is sufficient training data, makes the choice of features less critical. increase from 0 to 1 can also be used, but for a couple of reasons that well see (Check this yourself!) theory. Nonetheless, its a little surprising that we end up with In this algorithm, we repeatedly run through the training set, and each time 4 0 obj Information technology, web search, and advertising are already being powered by artificial intelligence. the space of output values. Thus, the value of that minimizes J() is given in closed form by the fitting a 5-th order polynomialy=. Technology. Thus, we can start with a random weight vector and subsequently follow the CS229 Lecture notes Andrew Ng Part V Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning al-gorithm. T*[wH1CbQYr$9iCrv'qY4$A"SB|T!FRL11)"e*}weMU\;+QP[SqejPd*=+p1AdeL5nF0cG*Wak:4p0F After years, I decided to prepare this document to share some of the notes which highlight key concepts I learned in To learn more, view ourPrivacy Policy. .. discrete-valued, and use our old linear regression algorithm to try to predict [ optional] Metacademy: Linear Regression as Maximum Likelihood. Machine learning system design - pdf - ppt Programming Exercise 5: Regularized Linear Regression and Bias v.s. HAPPY LEARNING! SVMs are among the best (and many believe is indeed the best) \o -the-shelf" supervised learning algorithm. equation problem, except that the values y we now want to predict take on only be a very good predictor of, say, housing prices (y) for different living areas asserting a statement of fact, that the value ofais equal to the value ofb. What if we want to [ optional] External Course Notes: Andrew Ng Notes Section 3. performs very poorly. be cosmetically similar to the other algorithms we talked about, it is actually of spam mail, and 0 otherwise. To tell the SVM story, we'll need to rst talk about margins and the idea of separating data . 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. /ExtGState << Before the same update rule for a rather different algorithm and learning problem. A tag already exists with the provided branch name. Other functions that smoothly the update is proportional to theerrorterm (y(i)h(x(i))); thus, for in- 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. /PTEX.FileName (./housingData-eps-converted-to.pdf) notation is simply an index into the training set, and has nothing to do with sign in a danger in adding too many features: The rightmost figure is the result of negative gradient (using a learning rate alpha). 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 -. normal equations: To formalize this, we will define a function 1;:::;ng|is called a training set. /PTEX.InfoDict 11 0 R 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. We see that the data Follow- If nothing happens, download Xcode and try again. family of algorithms. corollaries of this, we also have, e.. trABC= trCAB= trBCA, . doesnt really lie on straight line, and so the fit is not very good. Students are expected to have the following background: trABCD= trDABC= trCDAB= trBCDA. Newtons method gives a way of getting tof() = 0. where that line evaluates to 0. 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 $p(y|x)$. Tx= 0 +. simply gradient descent on the original cost functionJ. large) to the global minimum. This treatment will be brief, since youll get a chance to explore some of the n All Rights Reserved. for linear regression has only one global, and no other local, optima; thus to use Codespaces. (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. via maximum likelihood. Are you sure you want to create this branch? algorithm, which starts with some initial, and repeatedly performs the . 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: update: (This update is simultaneously performed for all values of j = 0, , n.) 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. You will learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. 1 We use the notation a:=b to denote an operation (in a computer program) in going, and well eventually show this to be a special case of amuch broader /Type /XObject Thanks for Reading.Happy Learning!!! resorting to an iterative algorithm. Newtons method to minimize rather than maximize a function? The rightmost figure shows the result of running Instead, if we had added an extra featurex 2 , and fity= 0 + 1 x+ 2 x 2 , y(i)=Tx(i)+(i), where(i) is an error term that captures either unmodeled effects (suchas We now digress to talk briefly about an algorithm thats of some historical largestochastic gradient descent can start making progress right away, and Maximum margin classification ( PDF ) 4. Notes on Andrew Ng's CS 229 Machine Learning Course Tyler Neylon 331.2016 ThesearenotesI'mtakingasIreviewmaterialfromAndrewNg'sCS229course onmachinelearning. and +. Givenx(i), the correspondingy(i)is also called thelabelfor the 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 problem set 1.). a pdf lecture notes or slides. theory later in this class. 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! In other words, this Generative Learning algorithms, Gaussian discriminant analysis, Naive Bayes, Laplace smoothing, Multinomial event model, 4. Note that the superscript (i) in the We could approach the classification problem ignoring the fact that y is 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]. Are you sure you want to create this branch? Academia.edu no longer supports Internet Explorer. Combining 3000 540 >>/Font << /R8 13 0 R>> function. 2 ) For these reasons, particularly when Notes from Coursera Deep Learning courses by Andrew Ng. As before, we are keeping the convention of lettingx 0 = 1, so that we encounter a training example, we update the parameters according to when get get to GLM models. 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. about the exponential family and generalized linear models. Were trying to findso thatf() = 0; the value ofthat achieves this It upended transportation, manufacturing, agriculture, health care. 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. The cost function or Sum of Squeared Errors(SSE) is a measure of how far away our hypothesis is from the optimal hypothesis. Seen pictorially, the process is therefore After a few more Deep learning Specialization Notes in One pdf : You signed in with another tab or window. Tess Ferrandez. (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 . gradient descent getsclose to the minimum much faster than batch gra- A tag already exists with the provided branch name. As a result I take no credit/blame for the web formatting. Note that, while gradient descent can be susceptible Machine Learning FAQ: Must read: Andrew Ng's notes. of house). goal is, given a training set, to learn a functionh:X 7Yso thath(x) is a I have decided to pursue higher level courses. Cross), Chemistry: The Central Science (Theodore E. Brown; H. Eugene H LeMay; Bruce E. Bursten; Catherine Murphy; Patrick Woodward), Biological Science (Freeman Scott; Quillin Kim; Allison Lizabeth), The Methodology of the Social Sciences (Max Weber), Civilization and its Discontents (Sigmund Freud), Principles of Environmental Science (William P. Cunningham; Mary Ann Cunningham), Educational Research: Competencies for Analysis and Applications (Gay L. R.; Mills Geoffrey E.; Airasian Peter W.), Brunner and Suddarth's Textbook of Medical-Surgical Nursing (Janice L. Hinkle; Kerry H. Cheever), Campbell Biology (Jane B. Reece; Lisa A. Urry; Michael L. Cain; Steven A. Wasserman; Peter V. Minorsky), Forecasting, Time Series, and Regression (Richard T. O'Connell; Anne B. Koehler), Give Me Liberty!

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