Web Development With Clojure shows you how to apply Clojure programming fundamentals to build real-world solutions. You'll develop all the pieces of a full web application in this powerful language. If you already have some familiarity with Clojure, you'll learn how to put it to serious practical use. If you're new to the language, the book provides just enough Clojure to get down to business.You'll learn the full process of web development using Clojure while getting hands-on experience with current tools, libraries, and best practices in the language. You'll develop Clojure apps with both the Light Table and Eclipse development environments. Rather than frameworks, Clojure development builds on rich libraries. You'll acquire expertise in the popular Ring/Compojure stack, and you'll learn to use the Liberator library to quickly develop RESTful services. Plus, you'll find out how to use ClojureScript to work in one language on the client and server sides.Throughout the book, you'll develop key components of web applications, including multiple approaches to database access. You'll create a simple guestbook app and an app to serve resources to users. By the end, you will have developed a rich Picture Gallery web application from conception to packaging and deployment.This book is for anyone interested in taking the next step in web development.Q&A with Dmitri SotnikovWhy did you write Web Development with Clojure?When I started using Clojure, I found that it took a lot of work to find all the pieces needed to put together a working application. There was very little documentation available on how to organize the code, what libraries to use, or how to package the application for deployment. Having gone through the process of figuring out what works, I thought that it would be nice to make it easier for others to get started.What are the advantages of using a functional language?Over the course of my career, I have developed a great appreciation for functional programming. I find that it addresses a number of shortcomings present in the imperative paradigm. For example, in a functional language any changes to the data are created via revisions to the existing data. So they only exist in the local scope. This fact allows us to safely reason about individual parts of the program in isolation, which is critical for writing and supporting large applications.Why use Clojure specifically?Clojure is a simple and pragmatic language that is designed for real-world usage. It combines the productivity of a high-level language with the excellent performance seen in languages like C# or Java. It's also very easy to learn because it allows you to use a small number of concepts to solve a large variety of problems.If I already have a preferred web development platform, what might I get out of this book?If you're using an imperative language, you'll get to see a very different approach to writing code. Even if you're not going to use Clojure as your primary language, the concepts you'll learn will provide you with new ways to approach problems.Is the material in the book accessible to somebody who is not familiar with Clojure?Absolutely. The book targets developers who are already familiar with the basics of web development and are interested in learning Clojure in this context. The book introduces just enough of the language to get you productive and allows you to learn by example.

    This practical introduction illuminates new features and capabilities, with scenarios demonstrating how the platform can meet the needs of your business.Based on beta software, this book provides the early, high-level information you need to begin preparing now for deployment and management. Topics include:Virtualization and cloud solutions Availability Provisioning and storage management Security and scalability Infrastructure options Server administration

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    It has saved me hours in troubleshooting complicated operations problems." -Trotter Cashion, cofounder, Mashion DevOps can help developers, QAs, and admins work together to solve Linux server problems far more rapidly, significantly improving IT performance, availability, and efficiency. To gain these benefits, however, team members need common troubleshooting skills and practices. In DevOps Troubleshooting: Linux Server Best Practices , award-winning Linux expert Kyle Rankin brings together all the standardized, repeatable techniques your team needs to stop finger-pointing, collaborate effectively, and quickly solve virtually any Linux server problem. Rankin walks you through using DevOps techniques to troubleshoot everything from boot failures and corrupt disks to lost email and downed websites. You'll master indispensable skills for diagnosing high-load systems and network problems in production environments. Rankin shows how to Master DevOps' approach to troubleshooting and proven Linux server problem-solving principles Diagnose slow servers and applications by identifying CPU, RAM, and Disk I/O bottlenecks Understand healthy boots, so you can identify failure points and fix them Solve full or corrupt disk issues that prevent disk writes Track down the sources of network problems Troubleshoot DNS, email, and other network services Isolate and diagnose Apache and Nginx Web server failures and slowdowns Solve problems with MySQL and Postgres database servers and queries Identify hardware failures-even notoriously elusive intermittent failures

    Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of "Learning Python" puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. "Learning Python," Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of "Learning Python" also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of "Learning Python" gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding."Learning Python," Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then "Learning Python," Second Edition is your best choice.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    Each idiom comes with a detailed description, example code showing the "wrong" way to do it, and code for the idiomatic, "Pythonic" alternative. *This version of the book is for Python 2.7.3+. There is also a Python 3.3+ version available.* "Writing Idiomatic Python" contains the most common and important Python idioms in a format that maximizes identification and understanding. Each idiom is presented as a recommendation to write some commonly used piece of code. It is followed by an explanation of why the idiom is important. It also contains two code samples: the "Harmful" way to write it and the "Idiomatic" way. * The "Harmful" way helps you identify the idiom in your own code. * The "Idiomatic" way shows you how to easily translate that code into idiomatic Python. This book is perfect for you: * If you're coming to Python from another programming language * If you're learning Python as a first programming language * If you're looking to increase the readability, maintainability, and correctness of your Python code What is "Idiomatic" Python? Every programming language has its own idioms. Programming language idioms are nothing more than the generally accepted way of writing a certain piece of code. Consistently writing idiomatic code has a number of important benefits: * Others can read and understand your code easily * Others can maintain and enhance your code with minimal effort * Your code will contain fewer bugs * Your code will teach others to write correct code without any effort on your part

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    But the principles and practices of software architecting–what the authors call the “science of hard decisions”–have been evolving for cloud, mobile, and other shifts. Now fully revised and updated, this book shares the knowledge and real-world perspectives that enable you to design for success–and deliver more successful solutions. In this fully updated Second Edition, you will: Learn how only a deep understanding of domain can lead to appropriate architecture Examine domain-driven design in both theory and implementation Shift your approach to code first, model later–including multilayer architecture Capture the benefits of prioritizing software maintainability See how readability, testability, and extensibility lead to code quality Take a user experience (UX) first approach, rather than designing for data Review patterns for organizing business logic Use event sourcing and CQRS together to model complex business domains more effectively Delve inside the persistence layer, including patterns and implementation.

    Learn how to leverage the Django web framework to its full potential in this advanced tutorial and reference. Endorsed by Django, Pro Django more or less picks up where The Definitive Guide to Django left off and examines in greater detail the unusual and complex problems that Python web application developers can face and how to solve them.Provides in-depth information about advanced tools and techniques available in every Django installation Runs the gamut from the theory of Django's internal operations to actual code that solves real-world problems for high-volume environments Goes above and beyond other books, leaving the basics behind Shows how Django can do things even its core developers never dreamed possible

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    With this hands-on guide, you'll learn how to write effective, idiomatic Python code by leveraging its best and possibly most neglected features. Author Luciano Ramalho takes you through Python's core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time.Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work"

    This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr