By scale, we mean having numbers of users in the millions and being built by large engineering teams.For mobile engineers, this book is a blueprint for modern app engineering approaches. For non-mobile engineers and managers, it is a resource with which to build empathy and appreciation for the complexity of world-class mobile engineering.

    Now, they build on their message with a bold, iconoclastic strategy for creating the ideal company culture—what they call "the calm company." Their approach directly attack the chaos, anxiety, and stress that plagues millions of workplaces and hampers billions of workers every day.Long hours, an excessive workload, and a lack of sleep have become a badge of honor for modern professionals. But it should be a mark of stupidity, the authors argue. Sadly, this isn’t just a problem for large organizations—individuals, contractors, and solopreneurs are burning themselves out the same way. The answer to better productivity isn’t more hours—it’s less waste and fewer things that induce distraction and persistent stress.It’s time to stop celebrating Crazy, and start celebrating Calm, Fried and Hansson assert.Fried and Hansson have the proof to back up their argument. "Calm" has been the cornerstone of their company’s culture since Basecamp began twenty years ago. Destined to become the management guide for the next generation, It Doesn't Have to Be Crazy at Work is a practical and inspiring distillation of their insights and experiences. It isn’t a book telling you what to do. It’s a book showing you what they’ve done—and how any manager or executive no matter the industry or size of the company, can do it too.

    From creating new data-driven products through to increasing operational efficiency, big data has the potential to makeyour organization both more competitive and more innovative.As this emerging field transitions from the bleeding edge to enterprise infrastructure, it's vital to understand not only the technologies involved, but the organizational and cultural demands of being data-driven.Written by O'Reilly Radar's experts on big data, this anthology describes:- The broad industry changes heralded by the big data era- What big data is, what it means to your business, and how to start solving data problems- The software that makes up the Hadoop big data stack, and the major enterprise vendors' Hadoop solutions- The landscape of NoSQL databases and their relative merits- How visualization plays an important part in data work

    Written in a frank but engaging style, Debuggingprovides simple, foolproof principles guaranteed to help find any bug quickly. This book makes those shelves of application-specific debugging books (on C++, Perl, Java, etc.) obsolete. It changes the way readers think about debugging, making those pesky problems suddenly much easier to find and fix. Illustrating the rules with real-life bug-detection war stories, the book shows readers how to: * Understand the system: how perceiving the ""roadmap"" can hasten your journey * Quit thinking and look: when hands-on investigation can’t be avoided * Isolate critical factors: why changing one element at a time can be an essential tool * Keep an audit trail: how keeping a record of the debugging process can win the day

    The rules? No marketing budget, no PR stunts, and it must be sustainably successful. No short-term fads.This is not a game of chance. It is a game of skill and strategy.And it begins with a single question: given competing products of equal pricing, promotion, and perceived quality, why does one outsell the others?The answer doesn’t live in the sustainably successful products or services. The answer lives in those who use them.Our goal is to craft a strategy for creating successful users. And that strategy is full of surprising, counter-intuitive, and astonishingly simple techniques that don’t depend on a massive marketing or development budget. Techniques typically overlooked by even the most well-funded, well-staffed product teams.Every role is a key player in this game. Product development, engineering, marketing, user experience, support—everyone on the team. Even if that team is a start-up of one. Armed with a surprisingly overlooked science and a unique POV, we can can reduce the role of luck. We can build sustainably successful products and services that rely not on unethical persuasive marketing tricks but on helping our users have deeper, richer experiences. Not just in the moments while they’re using our product but, more importantly, in the moments when they aren’t.

    More and more frequently, ScrumMasters and project managers are being asked to coach agile teams. But it's a challenging role. It requires new skills--as well as a subtle understanding of when to step in and when to step back. Migrating from "command and control" to agile coaching requires a whole new mind-set. In Coaching Agile Teams, Lyssa Adkins gives agile coaches the insights they need to adopt this new mind-set and to guide teams to extraordinary performance in a re-energized work environment. You'll gain a deep view into the role of the agile coach, discover what works and what doesn't, and learn how to adapt powerful skills from many allied disciplines, including the fields of professional coaching and mentoring. Coverage includes Understanding what it takes to be a great agile coach Mastering all of the agile coach's roles: teacher, mentor, problem solver, conflict navigator, and performance coach Creating an environment where self-organized, high-performance teams can emerge Coaching teams past cooperation and into full collaboration Evolving your leadership style as your team grows and changes Staying actively engaged without dominating your team and stunting its growth Recognizing failure, recovery, and success modes in your coaching Getting the most out of your own personal agile coaching journey Whether you're an agile coach, leader, trainer, mentor, facilitator, ScrumMaster, project manager, product owner, or team member, this book will help you become skilled at helping others become truly great. What could possibly be more rewarding?

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Its popularity among open-source developers makes Git a necessary tool for professional programmers, but it can also do wonders for your personal coding workflow. You’ll be able to experiment with new ideas, radically refactor existing code, and efficiently share changes with other developers—all without the slightest worry towards breaking your project.This comprehensive guide will walk you through the entire Git library, writing code and executing commands every step of the way. You'll create commits, revert snapshots, navigate branches, communicate with remote repositories, and experience core Git concepts first-hand.Designed for newcomers to distributed development, Ry's Git Tutorial presents this complex subject in simple terms that anyone can understand. Beginner and veteran programmers alike will find this book to be a fun, fast, and friendly introduction to Git-based revision control.

    In 1999, "Refactoring" revolutionized design by introducing an effective process for improving code. With the highly anticipated " Refactoring to Patterns ," Joshua Kerievsky has changed our approach to design by forever uniting patterns with the evolutionary process of refactoring.This book introduces the theory and practice of pattern-directed refactorings: sequences of low-level refactorings that allow designers to safely move designs to, towards, or away from pattern implementations. Using code from real-world projects, Kerievsky documents the thinking and steps underlying over two dozen pattern-based design transformations. Along the way he offers insights into pattern differences and how to implement patterns in the simplest possible ways.Coverage includes: A catalog of twenty-seven pattern-directed refactorings, featuring real-world code examples Descriptions of twelve design smells that indicate the need for this book s refactorings General information and new insights about patterns and refactoringDetailed implementation mechanics: how low-level refactorings are combined to implement high-level patterns Multiple ways to implement the same pattern and when to use each Practical ways to get started even if you have little experience with patterns or refactoring"Refactoring to Patterns" reflects three years of refinement and the insights of more than sixty software engineering thought leaders in the global patterns, refactoring, and agile development communities. Whether you re focused on legacy or greenfield development, this book will make you a better software designer by helping you learn how to make important design changes safely and effectively. "

    This book is a result of a quest. A quest to uncover what habits can be cultivated to become a better software engineer.

    These principles have revolutionized manufacturing and have been adopted by the most innovative product companies including Toyota and 3M. In 2003 the Poppendieck's published Lean Software Development: An Agile Toolkit which showed how these same lean principles can be successfully applied to software development. Since that publication the authors have increased their understanding of Lean and Agile problems faced by large organizations and have emerged as leading advocates for bringing Lean production techniques to software development. While their first book provides an introduction, theoretical advice and a reference to Lean, this follow-up incorporates their gained knowledge and understanding of what works and goes steps further to provide hands-on guidance for implementing a Lean system. Using historical case studies from prominent companies such as Polaris, Lockheed and Fujistu the authors prove the overall value of Lean practices and shows how to effectively apply these methods to software production.

    Itzik Ben-Gan explains key T-SQL concepts and helps you apply your knowledge with hands-on exercises. The book first introduces T-SQL's roots and underlying logic. Next, it walks you through core topics such as single-table queries, joins, subqueries, table expressions, and set operators. Then the book covers more-advanced data-query topics such as window functions, pivoting, and grouping sets. The book also explains how to modify data, work with temporal tables, and handle transactions, and provides an overview of programmable objects. Microsoft Data Platform MVP Itzik Ben-Gan shows you how to: Review core SQL concepts and its mathematical roots Create tables and enforce data integrity Perform effective single-table queries by using the SELECT statement Query multiple tables by using joins, subqueries, table expressions, and set operators Use advanced query techniques such as window functions, pivoting, and grouping sets Insert, update, delete, and merge data Use transactions in a concurrent environment Get started with programmable objects-from variables and batches to user-defined functions, stored procedures, triggers, and dynamic SQL

    You could spend years discovering solutions to them all, step by step as you encounter them. Or you can just look in here. All successful database applications are destined to eventually run into issues scaling up their performance. Peek into the future of your PostgreSQL database's problems today. Know the warning signs to look for, and how to avoid the most common issues before they even happen. Surprisingly, most PostgreSQL database applications evolve in the same way: Choose the right hardware. Tune the operating system and server memory use. Optimize queries against the database, with the right indexes. Monitor every layer, from hardware to queries, using some tools that are inside PostgreSQL and others that are external. Using monitoring insight, continuously rework the design and configuration. On reaching the limits of a single server, break things up; connection pooling, caching, partitioning, and replication can all help handle increasing database workloads. The path to a high performance database system isn't always easy. But it doesn't have to be mysterious with the right guide. This book is a clear, step-by-step guide to optimizing and scaling up PostgreSQL database servers. - Publisher.