Böcker av Dmitry Zinoviev

Immerse yourself in the intricate world of forgotten programming languages with Seven Obscure Languages in Seven Weeks. This comprehensive guide serves as a bridge to understanding and revitalizing legacy code, offering invaluable insights into the evolution of programming. With hands-on tutorials spanning languages from Forth and Simula to SNOBOL and m4, readers are equipped to maintain older systems and gain a broader perspective on problem-solving techniques. Whether you are a seasoned developer, a software historian, or just curious about the roots of modern coding, this book illuminates the rich tapestry of programming's past and sheds light on its present and future. Venture into overlooked and long-forgotten programming languages that once stood at the forefront of technological innovation. From the stack-oriented design of Forth to the early object-oriented experiences in Simula, bridge the ever-widening chasm between contemporary code and legacy systems. If you find yourself ensnared by the challenges of updating or maintaining older systems, this book is the lifeline. Unravel the fabric of seven programming languages by following practical tutorials and building small applications. Find out how Simula led to C++, what made APL so powerful, and why we still use m4 even to this day. Along the way, you'll broaden your problem-solving horizons, and develop diverse approaches to computation that still ripple through today's coding landscape. By the final chapter, you won't merely possess historical knowledge, you'll be equipped with production ready skills capable of tackling projects that interface with legacy code. Trace the evolutionary lineage of programming to gain a predictive edge in anticipating future trends. After all, this isn't just a nostalgic trip - it's a roadmap to the past, present, and future of coding. What You Need: Various software tools and compilers are available for enthusiasts eager to explore the once-forgotten languages detailed in this book. Guidance is provided primarily for Linux users on accessing these older programming languages. This collection includes languages like m4, integral to the GNU Autoconf system, and other languages incorporated into the GNU ecosystem, such as APL, Forth, and Simula. For those with a penchant for nostalgia, there is the SNOBOL4.2, which can run using the DOSBox MS-DOS emulator. KRoC, an Occam compiler, works only with 32-bit architectures or in a docker. Suffolk University maintains Starset's modern implementation. Readers can find links to repositories of these development tools, ensuring they can fully immerse themselves in this intriguing journey.

The purpose of this book is to bring together brief descriptions of more than four hundred unique transport systems. The book contains information on narrow-gauge railways and trams, as well as ropeways, overhead railways, and monorails, and several park railways located on the territory of modern Ukraine and Moldova. Also included are military narrow-gauge railways in Romania, built by the Russian army during the First World War. Due to the fact that material for this book was collected using the "crowdsourcing" method through the now-defunct "Younger Brother" website, the book in no way pretends to be complete, accurate, relevant, and scientific. Some data could be outdated, some may be inaccurate. Nevertheless, since this is the first experience of publishing a manuscript of this kind, I consider all the above disadvantages acceptable - provided that the book is not used by the readers to make vital decisions.

Go from messy, unstructured artifacts stored in SQL and NoSQL databases to a neat, well-organized dataset with this quick reference for the busy data scientist. Understand text mining, machine learning, and network analysis; process numeric data with the NumPy and Pandas modules; describe and analyze data using statistical and network-theoretical methods; and see actual examples of data analysis at work. This one-stop solution covers the essential data science you need in Python.Data science is one of the fastest-growing disciplines in terms of academic research, student enrollment, and employment. Python, with its flexibility and scalability, is quickly overtaking the R language for data-scientific projects. Keep Python data-science concepts at your fingertips with this modular, quick reference to the tools used to acquire, clean, analyze, and store data.This one-stop solution covers essential Python, databases, network analysis, natural language processing, elements of machine learning, and visualization. Access structured and unstructured text and numeric data from local files, databases, and the Internet. Arrange, rearrange, and clean the data. Work with relational and non-relational databases, data visualization, and simple predictive analysis (regressions, clustering, and decision trees). See how typical data analysis problems are handled. And try your hand at your own solutions to a variety of medium-scale projects that are fun to work on and look good on your resume.Keep this handy quick guide at your side whether you're a student, an entry-level data science professional converting from R to Python, or a seasoned Python developer who doesn't want to memorize every function and option.What You Need:You need a decent distribution of Python 3.3 or above that includes at least NLTK, Pandas, NumPy, Matplotlib, Networkx, SciKit-Learn, and BeautifulSoup. A great distribution that meets the requirements is Anaconda, available for free from www.continuum.io. If you plan to set up your own database servers, you also need MySQL (www.mysql.com) and MongoDB (www.mongodb.com). Both packages are free and run on Windows, Linux, and Mac OS.