S Guide to Network Programming. MySQL Commands. This is a list of handy MySQL commands that I use time and time again. At the bottom are statements, clauses, and functions you can use in MySQL. Below that are PHP and Perl API functions you can use to interface with MySQL. To use those you will need to build PHP with MySQL functionality. To use MySQL with Perl you will need to use the Perl modules DBI and DBD::mysql. Below when you see # it means from the unix shell. To login (from unix shell) use -h only if needed. # [mysql dir]/bin/mysql -h hostname -u root -p Create a database on the sql server. mysql> create database [databasename]; List all databases on the sql server. mysql> show databases; Switch to a database. mysql> use [db name]; To see all the tables in the db. mysql> show tables; To see database's field formats. mysql> describe [table name]; To delete a db. mysql> drop database [database name]; To delete a table. mysql> drop table [table name]; Show all data in a table. mysql> SELECT * FROM [table name]; Show unique records.
Sum column. or. Digital Data Collection course. Another year, another web scraping course. Taught through SSRMC at the University of Cambridge. Below are slides from all three sessions. In the course I tried to achieve the following:- Show how to connect R to resources online- Use loops and functions to iteratively access online content- How to work with APIs- How to harvest data manually using Xpath expressions. What's new? - Many more examples and practice tasks- Updated API usage- Some bug fixes (and probably many new bugs introduced) Slides from last year's course:- session one: the basics- session two: digging deeper- session three: scaling up- session four: APIs To leave a comment for the author, please follow the link and comment on his blog: Quantifying Memory.
What Consumers Learn Before Deciding to Buy: Representation Learning. Features form the basis for much of our preference modeling. When asked to explain one's preferences, features are typically accepted as appropriate reasons: this job paid more, that candidate supports tax reform, or it was closer to home. We believe that features must be the drivers since they so easily serve as rationales for past behavior. Choice modeling formalizes this belief by assuming that products and services are feature bundles with the value of the bundle calculated directly from the utilities of its separate features.
All that we need to know about a product or service can be represented as the intersection of its features, which is why it is called conjoint analysis. At first, this approach seems to work, but it does not scale well. Representation learning, on the other hand, is associated with deep neural networks, such as the h2o package discussed by John Chambers at the useR! What do consumers learn before deciding to buy? Are you thinking about a Smart Watch? I’m all about that bootstrap (’bout that bootstrap) As some of my regular readers may know, I'm in the middle of writing a book on introductory data analysis with R. I'm at the point in the writing of the book now where I have to make some hard choices about how I'm going to broach to topic of statistical inference and hypothesis testing.
Given the current climate against NHST (the journal Basic and Applied Social Psychology banned it) and my own personal preferences, I wasn't sure just how much to focus on classical hypothesis testing. I didn't want to burden my readers with spending weeks trying to learn the intricacies of NHST just to have them being told to forget everything they know about it and not be able to use it without people making fun of them. So I posed a question to twitter: "Is it too outlandish to not include the topic of parametric HTs in an intro book about data analysis. The four ways I created the CIs were: So, clearly the normal (basic) boot doesn’t make up for small sample sizes. Some final thoughts: share this: Hypothesis Testing: Fishing for Trouble.
Monday 23 March 2015 - 14:50 Introduction "Can you check if this is significant? " It was a seemingly innocuous question from a dangerous source: a semi data-literate scientist. The kind who believed, deep in his heart, that small p-values were "good" and large p-values were "erroneous". On this day, the man in question had come forth with a large, complex multivariate dataset. He'd manually combed the data, visually inspected it, and hand-picked a hypothesis. "I can, but I shouldn't. " Fishing for Trouble On the surface, the above appears pretty innocent.
The problem, of course, is in the looking. The conclusion, naturally, was absurd. The comic serves as a simple argument for the necessity of multiple corrections. In our example, abiding by the scientist's request commits the same error from another angle. "Can you check if the link between green jelly beans and acne is significant? " Applied Example Let's consider a (slightly) less abstract example. A simulation study Conclusion: References G. Machine Learning in R for beginners. Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns.
These tasks are learned through available data that were observed through experiences or instructions, for example. Machine learning hopes that including the experience into its tasks will eventually improve the learning. The ultimate goal is to improve the learning in such a way that it becomes automatic, so that humans like ourselves don't need to interfere any more. Machine learning has close ties with Knowledge Discovery, Data Mining, Artificial Intelligence and Statistics.
This small tutorial is meant to introduce you to the basics of machine learning in R: it will show you how to use R to work with the well-known machine learning algorithm called “KNN” or k-nearest neighbors. Step One. Machine learning typically starts from observed data. Or. Rankk.org. Login. Given a list of integers, e.g. Casper:Jambalaya Lyrics - LyricWikia - song lyrics, music lyrics. Casper 1 zu der 23 zu der 4Es macht bam-bam an der Tür5 zu der 67 zu der 8Das Biest ist nun wieder erwachtGuck, ich kann es,guck, ich kann esPlötzlich ist alles andersSie campen vor den Hallen und fragen nach AutogrammenKreischen meinen Namen,klappen dabei zusammenHaben Ohnmachtsanfälle,bevor wir angefangen habenKomm' aus 'nem Redneck-OrtLeben Nascar dort sowie Chevy, FordWo der Vetter Bob in 'nemMeth-Labor seine Päckchen kochtAllein mit Tape-Recorder dort in 'nem KinderzimmerGroßgezogen mit Peter Tosh, Dylan und Lynyrd SkynyrdSommer immer, nie WinterSand so rot wie HautZirpende Grillen klingen, Moskitos so groß wie 'ne FaustIch kam so daherStadt des Mardi Gras,Land aller Drum LinerCoo Coo wie Voodoo und Zuflucht heiß wie das Jambalaya Halleluja!
iSICP 1.1 - The Elements of Programming. A powerful programming language is more than just a means for instructing a computer to perform tasks. The language also serves as a framework within which we organize our ideas about processes. Thus, when we describe a language, we should pay particular attention to the means that the language provides for combining simple ideas to form more complex ideas. Every powerful language has three mechanisms for accomplishing this: primitive expressions, which represent the simplest entities the language is concerned with, means of combination, by which compound elements are built from simpler ones, and means of abstraction, by which compound elements can be named and manipulated as units. In programming, we deal with two kinds of elements: procedures and data.
In this chapter we will deal only with simple numerical data so that we can focus on the rules for building procedures. Expressions One kind of primitive expression you might type is a number. Exercise 1.1.1. Exercise 1.2.
Funnies. Programming. CS 61A Fall 2011: Structure and Interpretation of Computer Programs. *UNIX. Hacker News. Web. Writing. Open Source.