The Brainfuck Programming Language Brainfuck is the ungodly creation of Urban Müller, whose goal was apparently to create a Turing-complete language for which he could write the smallest compiler ever, for the Amiga OS 2.0. His compiler was 240 bytes in size. (Though he improved upon this later -- he informed me at one point that he had managed to bring it under 200 bytes.) I originally started playing around with Brainfuck because of my own interest in writing very small programs for x86 Linux. I also used it as a vehicle for writing a program that created ELF files. Eventually, however, I too succumbed to the Imp of the Perverse and wrote some actual Brainfuck programs of my own. The Language A Brainfuck program has an implicit byte pointer, called "the pointer", which is free to move around within an array of 30000 bytes, initially all set to zero. The Brainfuck programming language consists of eight commands, each of which is represented as a single character. Resources The Brainfuck archive. Brian RaiterMuppetlabs
The Stony Brook Algorithm Repository This WWW page is intended to serve as a comprehensive collection of algorithm implementations for over seventy of the most fundamental problems in combinatorial algorithms. The problem taxonomy, implementations, and supporting material are all drawn from my book The Algorithm Design Manual. Since the practical person is more often looking for a program than an algorithm, we provide pointers to solid implementations of useful algorithms, when they are available. Because of the volatility of the WWW, we provide local copies for many of the implementations. We encourage you to get them from the original sites instead of Stony Brook, because the version on the original site is more likely to be maintained. Many of these codes have been made available for research or educational use, although commercial use requires a licensing arrangement with the author. Use at your own risk.
Sometimes, The Better You Program, The Worse You Communicate. 'peSHIr' on Fri, 05 Jun 2009 10:39:57 GMT, sez: So *this* is why I communicate so horribly! ;-) 'Mike Woodhouse' on Fri, 05 Jun 2009 11:34:33 GMT, sez: My Wife [giving some typically incomplete instructions]: You know what I mean Me: I don't. My wife doesn't program. 'DylanW' on Fri, 05 Jun 2009 11:56:36 GMT, sez: I have never before felt like my chosen career was actually doing me harm. (Kidding. 'Doekman' on Fri, 05 Jun 2009 12:06:01 GMT, sez: So what you are saying is actually that you have less than average programming skills? 'wpfleischmann' on Fri, 05 Jun 2009 12:56:12 GMT, sez: re: (1) Human communication is a lossy medium, so it requires significant redundancy. 'Stephan L.' on Fri, 05 Jun 2009 13:19:35 GMT, sez: Like Doekman says, you can read this the other way around: here are four reasons why the better you communicate with typical human beings, the worse you are as a programmer :-) 'Kyle Lanser' on Fri, 05 Jun 2009 13:55:44 GMT, sez: I Disagree. her: Crab's until 10, right? false; 1.
Sequoia - Innovate or Die: The Rise of Microservices Software has emerged as the critical differentiator in every industry, from financial services to fashion, as “technology first” startups disrupt global markets. To stay alive, some of the biggest global enterprises we know are making a radical change in how they build and deliver software. The new model is called microservices, an approach where large applications are broken down into small, loosely coupled and composable autonomous pieces. Microservices have four main benefits: Agility. The concept of microservices is not new. Enterprises tried to replicate this with an approach called “service-oriented-architecture” that largely failed because the right building blocks for mass adoption were not yet in place. Containers. IT infrastructure divides like a cell every time a new standard of abstraction is universally adopted. At each stage, a full landscape of tools and platforms rises. Every transformation comes with challenges. Most tellingly, we see developers voting with their feet.
Metodi Statistici per l'Apprendimento Orario lezioni Materiale bibliografico: Il materiale sarà fornito dal docente sotto forma di dispense integrate da riferimenti bibliografici. Per colmare eventuali lacune in calcolo delle probabilità e statistica e ottimizzazione non lineare si consiglia la consultazione dei testi seguenti: Paolo Baldi, Calcolo delle probabilità e statistica (seconda edizione). Testi generali di apprendimento automatico: Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014. Obiettivi L'apprendimento automatico si occupa dello sviluppo di algoritmi per la costruzione di modelli predittivi sulla base di un insieme di osservazioni relative ad un dato fenomeno. Programma Link utili Esami L'esame consiste in un approfondimento teorico oppure un progetto pratico. Introduzione e inquadramento dell'argomento Notazione e definizioni rilevanti Dimostrazione di un risultato tecnico Considerazioni critiche. Avvisi