Git Pocket Guide

This pocket guide is the perfect on-the-job companion to Git, the distributed version control system. It provides a compact, readable introduction to Git for new users, as well as a reference to common commands and procedures for those of you with Git experience. Written for Git version 1.8.2, this handy task-oriented guide is organized around the basic version control functions you need, such as making commits, fixing mistakes, merging, and searching history. Examine the state of your project at earlier points in time Learn the basics of creating and making changes to a repository Create branches so many people can work on a project simultaneously Merge branches and reconcile the changes among them Clone an existing repository and share changes with push/pull commands Examine and change your repository’s commit history Access remote repositories, using different network protocols Get recipes for accomplishing a variety of common tasks

This pocket guide is the perfect on-the-job companion to Git, the distributed version control system. It provides a compact, readable introduction to Git for new users, as well as a reference to common commands and procedures for those of you with Git experience. Written for Git version 1.8.2, this handy task-oriented guide is organized around the basic version control functions you need, such as making commits, fixing mistakes, merging, and searching history. Examine the state of your project at earlier points in time Learn the basics of creating and making changes to a repository Create branches so many people can work on a project simultaneously Merge branches and reconcile the changes among them Clone an existing repository and share changes with push/pull commands Examine and change your repositoryâ??s commit history Access remote repositories, using different network protocols Get recipes for accomplishing a variety of common tasks

Are you looking for a new version control system? Perhaps what you're using now is too cumbersome, or you just want to try something new to manage a pet project. With Git by Ryan Hodson, you can get up and running with one of the fastest-spreading revision control systems out there. Complete with vivid diagrams, clear code samples, and a careful walk-through of primary features, this free e-book is your quick guide to how Git operates, what its advantages are, and how you can incorporate it into your own workflow. This updated and expanded second edition of Book provides a user-friendly introduction to the subject, Taking a clear structural framework, it guides the reader through the subject's core elements. A flowing writing style combines with the use of illustrations and diagrams throughout the text to ensure the reader understands even the most complex of concepts. This succinct and enlightening overview is a required reading for all those interested in the subject . We hope you find this book useful in shaping your future career & Business.

Get up to speed on Git for tracking, branching, merging, and managing code revisions. Through a series of step-by-step tutorials, this practical guide takes you quickly from Git fundamentals to advanced techniques, and provides friendly yet rigorous advice for navigating the many functions of this open source version control system. This thoroughly revised edition also includes tips for manipulating trees, extended coverage of the reflog and stash, and a complete introduction to the GitHub repository. Git lets you manage code development in a virtually endless variety of ways, once you understand how to harness the system’s flexibility. This book shows you how. Learn how to use Git for several real-world development scenarios Gain insight into Git’s common-use cases, initial tasks, and basic functions Use the system for both centralized and distributed version control Learn how to manage merges, conflicts, patches, and diffs Apply advanced techniques such as rebasing, hooks, and ways to handle submodules Interact with Subversion (SVN) repositories—including SVN to Git conversions Navigate, use, and contribute to open source projects though GitHub

This pocket guide presents the most crucial information about SQL in a compact and easily accessible format, covering the four commonly used SQL variants--Oracle, IBM DB2, Microsoft SQL Server, and MySQL. Topics include: Data manipulation statements (SELECT, DELETE, INSERT, UPDATE, MERGE) and transaction control statements (START TRANSACTION, SAVEPOINT, COMMIT, ROLLBACK). Common SQL functions (date, numeric, math, trigonometric, string, conversion, aggregate) Such topics as literals, NULLs, CASE expressions, datatype conversion, regular expressions, grouping and summarizing data, joining tables, and writing queries (hierarchical, recursive, union, flashback) and subqueries. Instead of presenting complex and confusing syntax diagrams, the book teaches by example, showing the SQL statements and options that readers are most like to use. All example data is available on the O'Reilly web site. "If you need fast, accurate SQL information, with examples for multiple database engines, be sure to check out this book."--Chris Kempster, Senior DBA and author of SQL Server 2000 for the Oracle DBA, www.chriskempster.com

Pro Git (Second Edition) is your fully-updated guide to Git and its usage in the modern world. Git has come a long way since it was first developed by Linus Torvalds for Linux kernel development. It has taken the open source world by storm since its inception in 2005, and this book teaches you how to use it like a pro. Effective and well-implemented version control is a necessity for successful web projects, whether large or small. With this book you’ll learn how to master the world of distributed version workflow, use the distributed features of Git to the full, and extend Git to meet your every need. Written by Git pros Scott Chacon and Ben Straub, Pro Git (Second Edition) builds on the hugely successful first edition, and is now fully updated for Git version 2.0, as well as including an indispensable chapter on GitHub. It’s the best book for all your Git needs.

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms