Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords?In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. Peer under the hood of the systems you already use, and learn how to use and operate them more effectively Make informed decisions by identifying the strengths and weaknesses of different tools Navigate the trade-offs around consistency, scalability, fault tolerance, and complexity Understand the distributed systems research upon which modern databases are built Peek behind the scenes of major online services, and learn from their architectures

    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.

    If you're looking for a book like that, put this one back on the shelf.Rework shows you a better, faster, easier way to succeed in business. Read it and you'll know why plans are actually harmful, why you don't need outside investors, and why you're better off ignoring the competition. The truth is, you need less than you think. You don't need to be a workaholic. You don't need to staff up. You don't need to waste time on paperwork or meetings. You don't even need an office. Those are all just excuses.  What you really need to do is stop talking and start working. This book shows you the way. You'll learn how to be more productive, how to get exposure without breaking the bank, and tons more counterintuitive ideas that will inspire and provoke you.With its straightforward language and easy-is-better approach, Rework is the perfect playbook for anyone who’s ever dreamed of doing it on their own. Hardcore entrepreneurs, small-business owners, people stuck in day jobs they hate, victims of "downsizing," and artists who don’t want to starve anymore will all find valuable guidance in these pages.

    If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to:Determine which data sources to use for collecting information Assess data quality and distinguish signal from noise Build basic data models to illuminate patterns, and assimilate new information into the models Cope with ambiguous information Design experiments to test hypotheses and draw conclusions Use segmentation to organize your data within discrete market groups Visualize data distributions to reveal new relationships and persuade others Predict the future with sampling and probability models Clean your data to make it useful Communicate the results of your analysis to your audience Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, Head First Data Analysis uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

    As they helped grow Google from a young start-up to a global icon, they relearned everything they knew about management. How Google Works is the sum of those experiences distilled into a fun, easy-to-read primer on corporate culture, strategy, talent, decision-making, communication, innovation, and dealing with disruption.The authors explain how the confluence of three seismic changes - the internet, mobile, and cloud computing - has shifted the balance of power from companies to consumers. The companies that will thrive in this ever-changing landscape will be the ones that create superior products and attract a new breed of multifaceted employees whom the authors dub 'smart creatives'. The management maxims ('Consensus requires dissension', 'Exile knaves but fight for divas', 'Think 10X, not 10%') are illustrated with previously unreported anecdotes from Google's corporate history.'Back in 2010, Eric and I created an internal class for Google managers,' says Rosenberg. 'The class slides all read 'Google confidential' until an employee suggested we uphold the spirit of openness and share them with the world. This book codifies the recipe for our secret sauce: how Google innovates and how it empowers employees to succeed.'

    You want to learn about the path I took that eventually led me to Zappos, and the lessons I learned along the way. You want to learn from all the mistakes we made at Zappos over the years so that your business can avoid making some of the same ones. You want to figure out the right balance of profits, passion, and purpose in business and in life. You want to build a long-term, enduring business and brand. You want to create a stronger company culture, which will make your employees and coworkers happier and create more employee engagement, leading to higher productivity. You want to deliver a better customer experience, which will make your customers happier and create more customer loyalty, leading to increased profits. You want to build something special. You want to find inspiration and happiness in work and in life. You ran out of firewood for your fireplace. This book makes an excellent fire-starter.

    Sold in over 85 countries and translated into more than 5 languages.---------------Want to get started on data science?Our promise: no math added.This book has been written in layman's terms as a gentle introduction to data science and its algorithms. Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application. To help you grasp key concepts, we stick to intuitive explanations and visuals.Popular concepts covered include:- A/B Testing- Anomaly Detection- Association Rules- Clustering- Decision Trees and Random Forests- Regression Analysis- Social Network Analysis- Neural NetworksFeatures:- Intuitive explanations and visuals- Real-world applications to illustrate each algorithm- Point summaries at the end of each chapter- Reference sheets comparing the pros and cons of algorithms- Glossary list of commonly-used termsWith this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.

    Every aircraft is equipped with an almost indestructible black box. When there is an accident, the box is opened, the data is analyzed, and the reason for the accident excavated. This ensures that procedures are adapted so that the same mistake doesn’t happen again. With this method, the industry has created an astonishing safety record.For pilots working in a safety-critical industry, getting it wrong can have deadly consequences. But most of us have a relationship with failure that impedes progress, halts innovation, and damages our lives. We don’t acknowledge it or learn from it —though we often think we do.Moving from anthropology to psychology and from history to complexity theory, Matthew Syed explains why even when we think we have 20/20 hindsight, our vision’s still fuzzy. He offers a radical new idea: that the most important determinant of success in any field, whether sports, business, or life, is an acknowledgment of failure and a willingness to engage with it. This is how we learn, progress and excel. This approach explains everything from biological evolution and the efficiency of markets to the success of the Mercedes F1 team and the mindset of David Beckham.Using a cornucopia of interviews, gripping stories, and sharp-edged science, Syed explores the intimate relationship between failure and success, and shows why we need to transport black box thinking into our own lives. If we wish to unleash our potential, we must diagnose and break free of our failures. Part manifesto for change, part intellectual adventure, this groundbreaking book reveals how to do both.

    The result is described as “a thought-provoking visual feast”.

    Rather, other factors can be even more crucial such as identifying our passions and following through on our commitments.Drawing on her own powerful story as the daughter of a scientist who frequently bemoaned her lack of smarts, Duckworth describes her winding path through teaching, business consulting, and neuroscience, which led to the hypothesis that what really drives success is not genius, but a special blend of passion and long-term perseverance. As a professor at the University of Pennsylvania, Duckworth created her own character lab and set out to test her theory.Here, she takes readers into the field to visit teachers working in some of the toughest schools, cadets struggling through their first days at West Point, and young finalists in the National Spelling Bee. She also mines fascinating insights from history and shows what can be gleaned from modern experiments in peak performance. Finally, she shares what she's learned from interviewing dozens of high achievers; from JP Morgan CEO Jamie Dimon to the cartoon editor of The New Yorker to Seattle Seahawks Coach Pete Carroll.Winningly personal, insightful, and even life-changing, Grit is a book about what goes through your head when you fall down, and how that not talent or luck makes all the difference.

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints--supported by research and the author's extensive experience with clients in industries all over the world--for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel.

    The Google Story takes you deep inside the company's wild ride from an idea that struggled for funding in 1998 to a firm that rakes in billions in profits, making Brin and Page the wealthiest young men in America. Based on scrupulous research and extraordinary access to Google, this fast-moving narrative reveals how an unorthodox management style and culture of innovation enabled a search engine to shake up Madison Avenue and Wall Street, scoop up YouTube, and battle Microsoft at every turn. Not afraid of controversy, Google is expanding in Communist China and quietly working on a searchable genetic database, initiatives that test the founders' guiding mantra: DON'T BE EVIL.

    It’s about getting only the right things done.  It is not  a time management strategy, or a productivity technique. It is a systematic discipline for discerning what is absolutely essential, then eliminating everything that is not, so we can make the highest possible contribution towards the things that really matter.  By forcing us to apply a more selective criteria for what is Essential, the disciplined pursuit of less empowers us to reclaim control of our own choices about where to spend our precious time and energy – instead of giving others the implicit permission to choose for us.Essentialism is not one more thing – it’s a whole new way of doing everything. A must-read for any leader, manager, or individual who wants to learn who to do less, but better, in every area of their lives, Essentialism  is a movement whose time has come.

    The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/