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Tutorials \ Processing.org. Index - CompTIA Marketplace. The 10 Best Coding Challenge Websites for 2018 – Coderbyte. At the end of 2016 I published the article: The 10 most popular coding challenge websites for 2017. The list was based on a few resources such as popular blog posts, Quora posts, articles, Google searches, and popular posts on forums like r/learnprogramming and Hacker News.

This updated 2018 list features 10 websites that offer the best coding challenges and resources to help new and intermediate developers improve their skills, prepare for interviews, and progress in their careers. The ordering of the list is based on level of difficulty (beginner to advanced). 1. Coderbyte Beginner — Intermediate Coderbyte provides 200+ coding challenges you can solve in an online editor using 10 different programming languages.

Aside from coding challenges, they provide courses in Algorithms & Data Structures, Web Development, and prep courses for coding bootcamps. 2. Codewars provides a large collection of coding challenges submitted and edited by their own community. 3. 4. 5. Intermediate — Advanced 6. Stack Overflow Blog - A destination for all things related to development at Stack Overflow. 22 Free Resources for Data Structures and Algorithms: Binary Search Algorithms, String Algorithms… When you first start out with a tech career, you’re going to need to learn data structures and algorithms. These are the basis behind computer programming, and every high-quality employer will make sure you understand them. For beginners, though, it can be difficult to understand. In this series of technical blog posts, we’ll be sharing the resources we’ve collected here at Make School.

In this post, we’ll share our resources for number bases, recursion and search algorithms, and string algorithms. Number bases Recursion and search algorithms String algorithms Read Stack Overflow’s answers to the question “What is unit testing?” Conclusion These resources are helpful, but they aren’t the only ways to learn data structures and algorithms. Opensource.com. Data Science Central. TechCrunch – Startup and Technology News. Techopedia - Where IT and Business Meet. Vidcode: Code your own projects & games. Data Science Central. Data Science Summarized in One Picture.

DSC Data Science Search Engine. Reading List for Data Scientists. Machine Learnings. 87473b00 0c3c 4da9 abe8 8453cd36a05d. ACSL Programming contest computer contest. A+ Computer Science - Computer Science Curriculum and Contest Materials. Grok Code Quest | Grok Learning. Learn to code - for free. Webmaker. GeekWire – Breaking News in Technology & Business. The Case of Mistaken Identity – A Data Detective Analysis - insightfulaccountant.com. For those of you who have read Insightful Accountant over the past years you will remember my ‘Data Detective’ stories which followed the pattern of a Sherlock Holmes style mystery. This article takes a different approach by looking at the ‘detecting’ aspects of the problem rather than ‘the story.’ Instead of starting with our own mystery, we begin today by looking at one of Agatha Christy’s stories brought to film one more time in 2017. I recently watched the re-make of Murder on the Orient Express1, the one in which Kenneth Branagh plays the lead character.

During one episode of questioning of a witness the great detective Hercule Poirot concludes that the Countess Andrenyi had intentionally altered her passport by smudging the first character of her name so as to make her name appear as ELENA rather than HELENA. It just happens that a women’s handkerchief with a monogrammed letter ‘H’ was found at the scene of the murder. The set-up process in no way insures compliance. How the Circle Line rogue train was caught with data. The Extraordinary Link Between Deep Neural Networks and the Nature of the Universe. In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition.

They’ve mastered the ancient game of Go and thrashed the best human players. But there is a problem. Today that changes thanks to the work of Henry Lin at Harvard University and Max Tegmark at MIT. First, let’s set up the problem using the example of classifying a megabit grayscale image to determine whether it shows a cat or a dog. Such an image consists of a million pixels that can each take one of 256 grayscale values. In the language of mathematics, neural networks work by approximating complex mathematical functions with simpler ones.

Now Lin and Tegmark say they’ve worked out why. Beetle Blocks - Visual code for 3D design. Java: The Legend. Get the free ebook The road from Java's first public alpha of 1.0 to today has been long—and full of technical advances, innovative solutions, and interesting complications. Along the way, Java has flourished and is now one of the world's most important and widely-used programming environments. Benjamin Evans, the Java editor for InfoQ and author of Java in a Nutshell, 6th edition, takes us on a journey through time: How Java has benefitted from early design decisions, including "Write Once, Run Anywhere" and an insistence on backward compatibilityThe impact of open sourceThe enormous success and continued importance of the Java Virtual Machine and platformThe rise of Enterprise JavaThe evolution of the Java developer community and ecosystemJava's continuing influence on new programming languagesJava's greatest triumphs and most heroic failuresThe future of Java, including Java 9, Project Panama, Project Valhalla, and the Internet of Things Ben Evans.

Too Poor To Succeed? WhatsApp Story - Infographic. It was year 1992 in Ukraine. It was the worst of times for the economy. Jan Koum was 16. His mother, hoping to find a better future for her son, decided to immigrate to the United States. There wasn’t a lot waiting for them in Mountain View, CA when they arrived, but a government subsidy helped them get food stamps and an apartment. Jan worked as a cleaner at a grocery story, his mother babysat. His father stayed behind in Ukraine hoping to join his family shortly. Create an infographic like this on Adioma By 18 Jan knew he wanted to learn to program.

Koum’s mother died of cancer in 2000. In 2007 Koum and Acton left Yahoo. Over tea in his Russian friend’s kitchen Koum explained his idea: show status updates next to people’s phone number’s in the address book. A month later, demoing the app to his friends, Koum was taking notes on the bugs and fixes it needed. In hindsight, founding stories like this can seem almost inevitable. Paul Ford: What Is Code? | Bloomberg. A computer is a clock with benefits. They all work the same, doing second-grade math, one step at a time: Tick, take a number and put it in box one. Tick, take another number, put it in box two. Tick, operate (an operation might be addition or subtraction) on those two numbers and put the resulting number in box one.

Tick, check if the result is zero, and if it is, go to some other box and follow a new set of instructions. You, using a pen and paper, can do anything a computer can; you just can’t do those things billions of times per second. Apple has always made computers; Microsoft used to make only software (and occasional accessory hardware, such as mice and keyboards), but now it’s in the hardware business, with Xbox game consoles, Surface tablets, and Lumia phones. So many things are computers, or will be. When you “batch” process a thousand images in Photoshop or sum numbers in Excel, you’re programming, at least a little. 2.1 How Do You Type an “A”? It’s simple now, right? Why so many data scientists are leaving their jobs. Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it… — Dan Ariely This quote is so apt.

Many junior data scientists I know (this includes myself) wanted to get into data science because it was all about solving complex problems with cool new machine learning algorithms that make huge impact on a business. This was a chance to feel like the work we were doing was more important than anything we’ve done before. However, this is often not the case. In my opinion, the fact that expectation does not match reality is the ultimate reason why many data scientists leave.

Every company is different so I can’t speak for them all but many companies hire data scientists without a suitable infrastructure in place to start getting value out of AI. Robert Chang gave a very insightful quote in his blog post giving advice to junior data scientists: But it doesn’t stop there. Learning Math for Machine Learning.

Vincent Chen is a student at Stanford University studying Computer Science. He is also a Research Assistant at the Stanford AI Lab. It’s not entirely clear what level of mathematics is necessary to get started in machine learning, especially for those who didn’t study math or statistics in school. In this piece, my goal is to suggest the mathematical background necessary to build products or conduct academic research in machine learning. These suggestions are derived from conversations with machine learning engineers, researchers, and educators, as well as my own experiences in both machine learning research and industry roles. To frame the math prerequisites, I first propose different mindsets and strategies for approaching your math education outside of traditional classroom settings.

To preface the piece, I acknowledge that learning styles/frameworks/resources are unique to a learner’s personal needs/goals— your opinions would be appreciated in the discussion on HN! Getting Started. Math for Machine Learning [Video] Would you like to learn a mathematics subject that is crucial for many high-demand lucrative career fields such as: Computer Science Data Science Artificial Intelligence If you're looking to gain a solid foundation in Machine Learning to further your career goals, in a way that allows you to study on your own schedule at a fraction of the cost it would take at a traditional university, this online course is for you. If you're a working professional needing a refresher on machine learning or a complete beginner who needs to learn Machine Learning for the first time, this online course is for you.

Why you should take this online course: You need to refresh your knowledge of machine learning for your career to earn a higher salary. You need to learn machine learning because it is a required mathematical subject for your chosen career field such as data science or artificial intelligence. When Bayes, Ockham, and Shannon come together to define machine learning. Writing an R package from scratch.

Anyone who has created their own R package has probably come across Hilary Parker’s awesome blogpost, that walks you through creating your very first R package. The comprehensive detail on everything R packages can be found in Hadley Wickham’s superb book. In this post I am going to walk through some of the developments in the package development space since Hilary wrote her blog four years ago, in particular focussing on the relatively recent usethis package. I’ve made the assumption for this following tutorial that you’re a sane individual and that you’re using the RStudio IDE.

My main motivation stemmed from Hadley’s tweet: The package I have created during the course of this blog can be found on my GitHub. Initial Setup Within this section we will assemble the bare bones of a package and is very similar to Hilary’s blog I linked to earlier. Step 0: Packages we need The three packages we require are devtools, roxygen2 and usethis. Step 1: Creating the package create_package("dogs") install() DVzine.org - The Dvorak Zine.

The Most in Demand Skills for Data Scientists. Ideas. Welcome back. Please sign in. Welcome back. {* #userInformationForm *} {* traditionalSignIn_emailAddress *} {* traditionalSignIn_password *} {* traditionalSignIn_signInButton *} {* /userInformationForm *} Please confirm the information below before signing in. {* #socialRegistrationForm *} {* socialRegistration_firstName *} {* socialRegistration_lastName *} {* socialRegistration_displayName *} {* socialRegistration_emailAddress *} {* providerName *} {* profileURL *} {* profilePreferredUsername *} {* profileIdentifier *} {* /socialRegistrationForm *} You're now signed in to O'Reilly.com. Please confirm the information below to create a new account. {* #registrationForm *} {* traditionalRegistration_firstName *} {* traditionalRegistration_lastName *} {* traditionalRegistration_displayName *} {* traditionalRegistration_emailAddress *} {* traditionalRegistration_password *} {* traditionalRegistration_passwordConfirm *} {* /registrationForm *} We'll send you a link to reset your password.

Gmail: correo electrónico y almacenamiento gratuitos de Google. AnalyticBridge - A Data Science Central Community. Association for Computing Machinery. The Association for Computing Machinery (ACM) is an international learned society for computing. It was founded in 1947, and is the world's largest scientific and educational computing society.[1] The ACM is a non-profit professional membership group,[2] with more than 100,000 members as of 2011[update]. Its headquarters are in New York City.[3] The ACM is an umbrella organization for academic and scholarly interests in computer science. Its motto is "Advancing Computing as a Science & Profession". History[edit] The ACM was founded in 1947 under the name Eastern Association for Computing Machinery, which was changed the following year to the Association for Computing Machinery.[4] Activities[edit] ACM is organized into over 171 local chapters and 37 Special Interest Groups (SIGs), through which it conducts most of its activities.

Services[edit] Publications[edit] Portal and Digital Library[edit] ACM adopted a hybrid Open Access (OA) publishing model in 2013. Membership grades[edit] Norman E. Top 5 Websites for Practicing Data structures and Algorithms for Coding Interviews Free. Are you preparing for Coding Interviews? If yes then you might know that there are a number of free online resources to practice important topics for coding Interviews e.g. data structure and algorithms, database and SQL, and others. These websites are equally useful for both, new programmers who are just learning the fundamentals and for experienced ones who are brushing up their coding skills for interviews.

I have been sharing useful resources for programming and technical interviews from a long time in this book. In past, I have shared some recommended books for coding interviews and some of the frequently asked programming interview questions from tech companies (see here). Thes websites are not just useful for anyone who is preparing for coding interview, but also to any programmer who seriously wants to improve their coding skill, which is the most important skill for a programmer.

This growing trend has made these resources even more sought after. 1. 2. 3. 4. 5. Online Web Tutorials. How Do Computers Work?: New Video Series Explains the Inner Workings of the Device You Use Every Day. OPEN SOURCE sourceforge. Google.ai. WEBOPEDIA: Online Tech Dictionary for IT Professionals.

Stack Overflow Developer Survey 2016 Results. MEDIUM best programming languages 2018. THINKFUL The Beginner’s Dilemma: Your First 100 Hours of Code. Peppermint OS.