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Learn Git Branching

Learn Git Branching
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In-depth introduction to machine learning in 15 hours of expert videos In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover-to-cover to gain both a theoretical and practical understanding of many important methods for regression and classification. If you decide to attempt the exercises at the end of each chapter, there is a GitHub repository of solutions provided by students you can use to check your work. Related

Le guide ultime et définitif sur la programmation orientée objet en Python à l’usage des débutants qui sont rassurés par les textes détaillés qui prennent le temps de tout expliquer. Partie 1. Prérequis à ce tuto bien chargé : comprendre parfaitement les mots clés les plus courants (conditions, tests, pass, etc);comprendre parfaitement les fonctions (et donc les paramètres avancées en Python);comprendre la notion de référence;connaitre les structures de données de base (string, int, list, dict, etc). Intro Il y a des tas de manières de programmer. Des styles. Des formes que l’on donne au code. En vérité, le point de vue n’est pas déterminant. Mais chaque point de vue possède des caractéristiques et des outils différents. Ce que vous allez voir est ce qu’on appelle la programmation orientée objet, ou POO. Quand vous avez appris la programmation, on vous a montré comment stocker des données dans des structures de données: les listesles chaînesles entiersles dictionnairesetc Et on vous a montré comment créer un comportement pour votre programme en utilisant des mots clés, puis plus tard en utilisant des fonctions pour regrouper ces mots clés. C’est tout. Qu’est-ce qu’un objet Méthodes

Static Site Generators A static website generator combines a markup language, such as Markdown or reStructuredText, with a templating engine such as Jinja, to produce HTML files. The HTML files can be hosted and served by a web server or content delivery network (CDN)without any additional dependencies such as a WSGI server. Why are static site generators useful? Static content files such as HTML, CSS and JavaScript can be served from a content delivery network (CDN) for high scale and low cost. For example, when Full Stack Python was on the top of Hacker News for a weekend, GitHub Pages was used as a CDN to serve the site and didn't have any issues even with close to 400 concurrent connections at a time, as shown in the following Google Analytics screenshot captured during that traffic burst. How do static website generators work? Static site generators allow a user to create HTML files by writing in a markup language and coding template files. What's the downside to using static site generators? flow · @mdo The term GitHub flow refers to how one can develop software with GitHub. It's a process that minimizes friction, enables asynchronous communication (and development), and is decently flexible. For an overview, read Understanding the GitHub Flow on GitHub Guides. Applying the GitHub flow to development of The Website™ builds on that, and tends to go something like this. 1. Issues on the github/github repository are the easiest way to find things to do. It's worth mentioning though that we sometimes jump right into code from a meatspace discussion or from a tweet. Bottom line? 2. At GitHub, almost every new contribution happens in a branch. My branch naming scheme is sometimes serious and relevant, and usually not useful in the slightest. sidebar_hit_areas and today_we_celebrate_our_independence_day are both branch names that I've recently worked on. Great branches really should include accurate names, but most of the time it doesn't matter because they're so short lived. 3. 4.

R-bloggers | R news and tutorials contributed by (573) R bloggers Exemples de bons codes Python Yeah, on a des fannnnnnns ! Ça fait quelques semaines que je me suis mis à python. J’ai commencé par des scripts (tendance sysadmin oblige) puis je me suis lancé dans des choses un (petit) peu plus importantes, notamment influencé par les cours sur la POO. Je tiens d’ailleurs à vous féliciter sur ce point, même le site du zéro n’avait jamais réussi à me les faire vraiment comprendre. Cher [censored], Être placé au dessus du site du zéro provoque chez moi une érection incontrôlée. Bonne nouvelle, il n’existe rien de tel que le code parfait, et des tas d’excellents dev font de la merde quotidiennement, ce qui permet de relativiser face à son niveau. Maintenant, si je devais donner des exemples de code et doc dont on peut s’inspirer, je dirais : les modules strings et structs de batbelt sont faciles pour commencer et propres. Petit rappel ceci dit : même un bon code a toujours des lacunes. A éviter niveau code : django, bottle, twisted et les frameworks web en général.

Development Environments A development environment is a combination of a text editor and the Python interpreter. The text editor allows you to write the code. The interpreter provides a way to execute the code you've written. A text editor can be as simple as Notepad on Windows or more complicated as a complete integrated development environment (IDE) such as PyCharm which runs on any major operating system. Why is a development environment necessary? Python code needs to be written, executed and tested to build applications. While you're learning about development environments be sure to check out information on Vim and Emacs. A development environment example Here's what I (the author of Full Stack Python, Matt Makai) use to develop most of my Python applications. That's a common set up but you can certainly write great code with a much less expensive set up or a cloud-based development environment. Open source text editors Proprietary (closed source) editors Python-specific IDEs Development environment resources

Short-refcard.pdf Pour arrêter de galérer avec Git « Développeur web freelance J'adore Git ! Depuis 5 ans que je l'utilise quotidiennement, je ne me lasse pas d'admirer la puissance sublime de cet outil, et je ne compte plus les fois ou ma vie fut sauvée par l'une ou l'autre de ces obscures mais miraculeuses commandes. D'ailleurs, n'est-ce pas Aristote qui as dit « Donnez-moi vim et git, et je soulèverai le monde » ? Ce n'est pas un hasard si en Swahili, « Git » signifie « divinité toute puissante à la sagacité du renard, la volupté de l'hippopotame et la virilité du bonobo ». Je dois pourtant reconnaître que Git n'est pas forcément l'outil le plus abordable qui soit. C'est surtout après avoir eu l'occasion de donner une formation de 2 jours sur Git récemment que j'ai pu vraiment approfondir certains concepts, résoudre un certain nombre de « WTF ?! Tenez, prenez l'exemple suivant : Comment j'annule une modification d'un fichier ? git checkout Ok, comment je change de branche ? Ok, et comment je créé une nouvelle branche ? Mmm… Ok, et comment je supprime une branche ?

Stream Processing with Apache Flink | Coding It’s been a while since I wrote my last article. A Big-Data-Sorry to my “massive” audience. Actually, I was planning to write a follow-up to the last article on Machine Learning but could not find enough time to do it. Also, I’ll soon give a presentation on Apache Spark in a Meetup (Cologne, Germany). A classical example on what happens when you have to complete several tasks at the same time. As always, the sources are on GitHub. What is Apache Flink? Well, if you already know Apache Spark then the definition may be fairly similar: an open-source platform for distributed (cluster-computing) stream and batch data processing. The Flink Stack The Flink Stack is based on a single runtime which is split into two parts: batch processing part and streaming part. For Flink Batch-Processing is only a sub-type of Stream-ProcessingFlink implements its own memory management and serializersExactly-once semantics for stateful computations in streaming applications Execution Model Installation

Useful new R packages for data visualization and analysis The following is from a hands-on session I led at the recent Computer Assisted Reporting conference. There's a lot of activity going on with R packages now because of a new R development package called htmlwidgets, making it easy for people to write R wrappers for existing JavaScript libraries. The first html-widget-inspired package I want us to demo is Leaflet for R. If you’re not familiar with Leaflet, it’s a JavaScript mapping package. devtools::install_github("rstudio/leaflet") Load the library library("leaflet") Step 1: Create a basic map object and add tiles mymap <- leaflet() mymap <- addTiles(mymap) View the empty map by typing the object name: mymap Step 2: Set where you want the map to be centered and its zoom level mymap <- setView(mymap, -84.3847, 33.7613, zoom = 17) mymap Add a pop-up addPopups(-84.3847, 33.7616, 'Data journalists at work, <b>NICAR 2015</b>') And now I’d like to introduce you to a somewhat new chaining function in R: %>% View the finished product: Not a big deal?