coursera gcp

school and may not be used in future work, such as Scheme, Haskell, and Prolog .. In the future, we may not have the motivation to do this. If I only write C ++/Java/Python in my life, is there anything more terrible than this. ====== ====== The problem has been updated. just add a few words. 1. about the class. I have always recommended MOOC for learning. if English is good, Coursera and edX are preferred. english is not good, and NetEase/ is

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning foundations)-Job three q18-20 C + + implementation. Although there are many great gods in many blogs have given the implementation of Phython, but given the C + + implementation of the article is significantly less, here for everyone to provide a C + + implementation of the idea! Although my code can get the correct answer, but

developers quickly apply to the project.8. The Magicalrecord author is an IOS engineer at Coursera, which is inspired by the Active Record of Ruby on Rails and is primarily designed to facilitate operation CoreData, helping to clear CoreData Use the code to help facilitate CoreData's work9. FMDB a library for SQLite encapsulation, easy and easy to useMantle author is a GITHUB employee and the documentation is very clear: mantle makes it easy-to-write

as a probability. This is a good add-on effect, for example, you can use it to rank instead of classify.Even if you do not want logistic regression 100% to work, you can also do yourself a favor by running a simple L2 regularization logistic regression as a baseline before using the "fancier" approach.Well, now that you've set up a logistic regression baseline, the next thing you should do, I'll basically recommend two possible directions: Support vector Machine (SVM) or decision tree integrati

community can be completely satisfied with you. All kinds of programmer problems only you can not imagine, no you can not find.(http://www. jikexueyuan.com) (http://www. imooc.com) If you prefer to watch videos this is a more in-band learning, and the two sites are not wrong. A huge amount of instructional videos, as well as a more comprehensive learning route wiki, can help you on your way to learning programming. To live and learn to be old. Learn it with your heart. Thanks to the user @ Pu

do not have a very systematic way to read a book, so I also search, or really know what others recommend, so, according to their own situation, suitable for their own books to see; The Busy coder ' s Guide to Android Development This is Daniel Commonsguy open source Book, it's updated very timely, basically follow the SDK update, download PDF can be here (Four-to-free Guarantee) Download, the source code in the book can be viewed in GitHub (commonsguy/cw-omnibus); Best Android Boo

greatly expand your horizons, open up your gap with ordinary people."Structure and interpretation of Computer Programs", the introduction of software engineering book, this book must be read, you can not finish the title, the effect ibid."Computer Systems:a Programmer's Perspective", the computer system (software and hardware) introductory book.Dragon Book as an introductory book, Write is too bad, it is said that the Stanford compiler Coursera on th

problem was equivalent to finding the topological order in a directed graph. If a cycle exists, no topological ordering exists and therefore it'll be impossible to take all courses. Topological Sort via dfs-a great video tutorial (minutes) on Coursera explaining the basic concepts of topological so Rt. Topological sort could also is done via BFS. Links: http://leetcode.com/problems/course-schedule-ii/ExercisesLike course schedule,

in many cases, non-university graduates have become able-to-recruit people."As long as you can program," says a hiring executive at Nicole Tucker,icims. "As long as you have the ability to solve problems, full of curiosity, then you can try." Tucker added that iCIMS hired programmers to be self-taught in Codecademy and Coursera. Tucker says she attaches great importance to the motivation of candidates. But now the problem is that many people choose t

Videos CourseMany people start to learn from the machine through video resources. I saw a lot of video resources related to machine learning on YouTube and Videolectures. The problem with this is that you may just watch the video and not actually do it. My suggestion is that when you watch the video, you should take more notes, and then you will discard your notes in time. At the same time, I suggest you put the learned things into practice.Frankly speaking, I don't see video resources that are

logistic regression is used, the loss function can form a convex function.Polynomial spline function fittingPolynomial fitting, the model is a polynomial form, the spline function, the model is not only continuous, but also at the boundary, the derivatives number is continuous. Benefits: is a smooth curve that avoids the appearance of turbulence in the boundary (Runge linear)Http://baike.baidu.com/view/301735.htmHere are a few concepts that need to be understood slowly:Unstructured predictive M

Statement Although before listening to the compression perception and sparse expression, in fact, yesterday only formally started to understand, purely novice, if there are errors, please point out the common progress. The main learning materials are Coursera open classes at Duke University--image and video processing, by Pro.guillermo Sapiro the 9th lesson. Because of the understanding of image processing also comes from the course,

build a model from a browser. Pick out a platform and use it when you actually learn machine learning. Do not talk on paper, to practice!Video Courses Videos CourseMany people start to learn from the machine through video resources. I saw a lot of video resources related to machine learning on YouTube and Videolectures. The problem with this is that you may just watch the video and not actually do it. My suggestion is that when you watch the video, you should take more notes, and then you

be struggling. So the bean leaf emphasizes the importance of a good foundation. Once you have mastered the basics of mathematics, your understanding of these models can easily transcend the formula itself.The difference between deep knowledge and shallow knowledgeBean leaves think that when we learn knowledge, we should learn to differentiate, what is deep knowledge (knowledge), what is shallow knowledge (shallow knowledge).Some knowledge is shallow knowledge, only need to remember to know. But

System Linux/ubuntu 14.04.5 LTS (trusty Tahr) + ROS Indigo Linux/ubuntu 16.04.1 LTS (xenial Xerus) + ROS Kinetic Web Explorer Google Chrome C + + IDE Xcode (MAC) Netbeans Qtcreator Codeblocks (Windows) Python IDE Spyder Pycharm Java IDE IntelliJ idea Eclipse Math/matrix computation Matlab Octave Editor Sublime Text 3 notepad++ (Windows) Gedit Vi/vim Emac D

(\theta) =-\frac{1}{m} \sum\limits_{i=1}^{m}\left[y^{(i)}\log (H_\theta (x^{(i)}) + (1-y^{(i)}) \log (1-h_\ Theta (x^{(i)})) \right] + \frac{\lambda}{2m}\sum\limits_{j=1}^{n}\theta_j^{2}$$Gradient Descent parameter update:$$\theta_0 = \theta_0-\alpha\frac{1}{m}\sum\limits_{i=1}^{m} (H_\theta (x^{(i)})-y^{(i)}) x_0^{(i)}; j = 0$$$$\theta_j = \theta_j-\alpha \left[\frac{1}{m}\sum\limits_{i=1}^{m} (H_\theta (x^{(i)})-y^{(i)}) x_j^{(i)} + \frac{\ Lambda}{m}\theta_j \right]; J > 1$$Reference documen

The predecessor of the network said: machine learning is not an isolated algorithm piled up, want to look like "Introduction to the algorithm" to see machine learning is an undesirable method. There are several things in machine learning that have been running through the book, such as the distribution of data, the maximum likelihood (and several methods of finding extreme values, but this is more mathematically), the tradeoffs of deviations, variances, and the knowledge of feature selection, mo

linear kernel)The neural network works well in all kinds of n, m cases, and the defect is that the training speed is slow.Reference documents[1] Andrew Ng Coursera public class seventh week[2] Kernel Functions for machine learning applications. http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#chisquare[3] wikipedia:string kernel. Https://en.wikipedia.org/wiki/String_kernel[4] Hofmann T, Schölkopf B, Smola A J. Kernel met

calculates the accuracy of the entire system at this time: As shown in, text recognition consists of four parts. Now we can find the system accuracy after optimization for each part. The question is, how can we improve the accuracy of the entire system? We can see from the table that, if we have optimized the text moderation part, the accuracy will be72%Add89%If we optimize the character segmentation, the accuracy is only from89%To90%If character recognition is optimized90%To100%In contr

omnipotent, you can find a variety of learning programming resources, such as Coursera and Andudacity's entry-level courses. If you've never written a code, try these free courses and tutorials to get a feel for yourself. There are at least two benefits to finding a job that you really like. The first is that it is more fun to work because of interest than to work because you earn money. The second is, if you really like programming, then you will h