Download or read book Addison Wesley Study Series written by Addison-Wesley Publishing Company and published by . This book was released on 1957 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Learning React written by Kirupa Chinnathambi and published by Addison-Wesley Professional. This book was released on 2018-04-26 with total page 747 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning React A hands-on guide to building web applications using React and Redux As far as new web frameworks and libraries go, React is quite the runaway success. It not only deals with the most common problems developers face when building complex apps, it throws in a few additional tricks that make building the visuals for such apps much, much easier. What React isn’t, though, is beginner-friendly and approachable. Until now. In Learning React, author Kirupa Chinnathambi brings his fresh, clear, and very personable writing style to help web developers new to React understand its fundamentals and how to use it to build really performant (and awesome) apps. The only book on the market that helps you get your first React app up and running in just minutes, Learning Reactis chock-full of colorful illustrations to help you visualize difficult concepts and practical step-by-step examples to show you how to apply what you learn. Build your first React app Create components to define parts of your UI Combine components into other components to build more complex UIs Use JSX to specify visuals without writing full-fledged JavaScript Deal with maintaining state Work with React’s way of styling content Make sense of the mysterious component lifecycle Build multi-page apps using routing and views Optimize your React workflow using tools such as Node, Babel, webpack, and others Use Redux to make managing your app data and state easy Contents at a Glance 1 Introducing React 2 Building Your First React App 3 Components in React 4 Styling in React 5 Creating Complex Components 6 Transferring Properties 7 Meet JSX... Again! 8 Dealing with State in React 9 Going from Data to UI in React 10 Events in React 11 The Component Lifecycle 12 Accessing DOM Elements in React 13 Setting Up Your React Dev Environment 14 Working with External Data in React 15 Building an Awesome Todo List App in React 16 Creating a Sliding Menu in React 17 Avoiding Unnecessary Renders in React 18 Creating a Single-Page App in React Using React Router 19 Introduction to Redux 20 Using Redux with React
Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book An Introduction To Chaotic Dynamical Systems written by Robert Devaney and published by CRC Press. This book was released on 2018-03-09 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of nonlinear dynamical systems has exploded in the past 25 years, and Robert L. Devaney has made these advanced research developments accessible to undergraduate and graduate mathematics students as well as researchers in other disciplines with the introduction of this widely praised book. In this second edition of his best-selling text, Devaney includes new material on the orbit diagram fro maps of the interval and the Mandelbrot set, as well as striking color photos illustrating both Julia and Mandelbrot sets. This book assumes no prior acquaintance with advanced mathematical topics such as measure theory, topology, and differential geometry. Assuming only a knowledge of calculus, Devaney introduces many of the basic concepts of modern dynamical systems theory and leads the reader to the point of current research in several areas.
Download or read book Machine Learning in Production written by Andrew Kelleher and published by Addison-Wesley Professional. This book was released on 2019-02-27 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projects Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualization techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book Machine Learning with Python for Everyone written by Mark Fenner and published by Addison-Wesley Professional. This book was released on 2019-07-30 with total page 1376 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning. Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use. Understand machine learning algorithms, models, and core machine learning concepts Classify examples with classifiers, and quantify examples with regressors Realistically assess performance of machine learning systems Use feature engineering to smooth rough data into useful forms Chain multiple components into one system and tune its performance Apply machine learning techniques to images and text Connect the core concepts to neural networks and graphical models Leverage the Python scikit-learn library and other powerful tools Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book Operating System Concepts 10e Abridged Print Companion written by Abraham Silberschatz and published by John Wiley & Sons. This book was released on 2018-01-11 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: The tenth edition of Operating System Concepts has been revised to keep it fresh and up-to-date with contemporary examples of how operating systems function, as well as enhanced interactive elements to improve learning and the student’s experience with the material. It combines instruction on concepts with real-world applications so that students can understand the practical usage of the content. End-of-chapter problems, exercises, review questions, and programming exercises help to further reinforce important concepts. New interactive self-assessment problems are provided throughout the text to help students monitor their level of understanding and progress. A Linux virtual machine (including C and Java source code and development tools) allows students to complete programming exercises that help them engage further with the material. The Print Companion includes all of the content found in a traditional text book, organized the way you would expect it, but without the problems.
Download or read book More Agile Testing written by Janet Gregory and published by Pearson Education. This book was released on 2015 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: Janet Gregory and Lisa Crispin pioneered the agile testing discipline with their previous work, Agile Testing. Now, in More Agile Testing, they reflect on all they've learned since. They address crucial emerging issues, share evolved agile practices, and cover key issues agile testers have asked to learn more about. Packed with new examples from real teams, this insightful guide offers detailed information about adapting agile testing for your environment; learning from experience and continually improving your test processes; scaling agile testing across teams; and overcoming the pitfalls of automated testing. You'll find brand-new coverage of agile testing for the enterprise, distributed teams, mobile/embedded systems, regulated environments, data warehouse/BI systems, and DevOps practices. You'll come away understanding - How to clarify testing activities within the team - Ways to collaborate with business experts to identify valuable features and deliver the right capabilities - How to design automated tests for superior reliability and easier maintenance - How agile team members can improve and expand their testing skills - How to plan "just enough," balancing small increments with larger feature sets and the entire system - How to use testing to identify and mitigate risks associated with your current agile processes and to prevent defects - How to address challenges within your product or organizational context - How to perform exploratory testing using "personas" and "tours" - Exploratory testing approaches that engage the whole team, using test charters with session- and thread-based techniques - How to bring new agile testers up to speed quickly-without overwhelming them The eBook edition of More Agile Testing also is available as part of a two-eBook collection, The Agile Testing Collection (9780134190624).
Download or read book The Lean Mindset written by Mary Poppendieck and published by Addison-Wesley. This book was released on 2013-09-20 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: What company doesn’t want energized workers, delighted customers, genuine efficiency, and breakthrough innovation? The Lean Mindset shows how lean companies really work–and how a lean mindset is the key to creating stunning products and delivering amazing services. Through cutting-edge research and case studies from leading organizations, including Spotify, Ericsson, Intuit, GE Healthcare, Pixar, CareerBuilder, and Intel, you’ll discover proven patterns for developing that mindset. You’ll see how to cultivate product teams that act like successful startups, create the kind of efficiency that attracts customers, and leverage the talents of bright, creative people. The Poppendiecks weave lean principles throughout this book, just as those principles must be woven throughout the fabric of your truly lean organization. Learn How To Start with an inspiring purpose, and overcome the curse of short-term thinking Energize teams by providing well-framed challenges, larger purposes, and a direct line of sight between their work and the achievement of those purposes Delight customers by gaining unprecedented insight into their real needs, and building products and services that fully anticipate those needs Achieve authentic, sustainable efficiency without layoffs, rock-bottom cost focus, or totalitarian work systems Develop breakthrough innovations by moving beyond predictability to experimentation, beyond globalization to decentralization, beyond productivity to impact Lean approaches to software development have moved from novelty to widespread use, in large part due to the principles taught by Mary and Tom Poppendieck in their pioneering books. Now, in The Lean Mindset, the Poppendiecks take the next step, looking at a company where multidiscipline teams are expected to ask the right questions, solve the right problems, and deliver solutions that customers love.
Download or read book Set Theory written by Charles C. Pinter and published by . This book was released on 1971 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Managing Knowledge written by Wayne Applehans and published by Addison-Wesley Professional. This book was released on 1999 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge management is a hot topic in the modern corporate world and this unique, practical book helps managers utilize corporate information to achieve real gains in productivity and profitability.
Download or read book Newtonian Tasks Inspired by Physics Education Research written by C. Hieggelke and published by Pearson. This book was released on 2011-01-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Resource added for the Physics ?10-806-150? courses.
Download or read book Foundations of Deep Reinforcement Learning written by Laura Graesser and published by Addison-Wesley Professional. This book was released on 2019-11-20 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book Code That Fits in Your Head written by Mark Seemann and published by Addison-Wesley Professional. This book was released on 2021-11-02 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: How to Reduce Code Complexity and Develop Software More Sustainably "Mark Seemann is well known for explaining complex concepts clearly and thoroughly. In this book he condenses his wide-ranging software development experience into a set of practical, pragmatic techniques for writing sustainable and human-friendly code. This book will be a must-read for every programmer." -- Scott Wlaschin, author of Domain Modeling Made Functional Code That Fits in Your Head offers indispensable, practical advice for writing code at a sustainable pace and controlling the complexity that causes projects to spin out of control. Reflecting decades of experience helping software teams succeed, Mark Seemann guides you from zero (no code) to deployed features and shows how to maintain a good cruising speed as you add functionality, address cross-cutting concerns, troubleshoot, and optimize. You'll find valuable ideas, practices, and processes for key issues ranging from checklists to teamwork, encapsulation to decomposition, API design to unit testing. Seemann illuminates his insights with code examples drawn from a complete sample project. Written in C#, they're designed to be clear and useful to anyone who uses any object-oriented language including Java , C++, and Python. To facilitate deeper exploration, all code and extensive commit messages are available for download. Choose mindsets and processes that work, and escape bad metaphors that don't Use checklists to liberate yourself, improving outcomes with the skills you already have Get past “analysis paralysis” by creating and deploying a vertical slice of your application Counteract forces that lead to code rot and unnecessary complexity Master better techniques for changing code behavior Discover ways to solve code problems more quickly and effectively Think more productively about performance and security If you've ever suffered through bad projects or had to cope with unmaintainable legacy code, this guide will help you make things better next time and every time. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Download or read book Learning Regular Expressions written by Ben Forta and published by Addison-Wesley Professional. This book was released on 2018-05-01 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to use one of the most powerful text processing and manipulation tools available Regular expression experts have long been armed with an incredibly powerful tool, one that can be used to perform all sorts of sophisticated text processing and manipulation in just about every language and on every platform. That’s the good news. The bad news is that for too long, regular expressions have been the exclusive property of only the most tech savvy. Until now. Ben Forta's Learning Regular Expressions teaches you the regular expressions that you really need to know, starting with simple text matches and working up to more complex topics, including the use of backreferences, conditional evaluation, and look-ahead processing. You’ll learn what you can use, and you’ll learn it methodically, systematically, and simply. Regular expressions are nowhere near as complex as they appear to be at first glance. All it takes is a clear understanding of the problem being solved and how to leverage regular expressions to solve them. Read and understand regular expressions Use literal text and metacharacters to build powerful search patterns Take advantage of advanced regular expression features, including lookahead and backreferences Perform powerful search-and-replace operations in all major professional editing tools Add sophisticated form and text processing to web applications Search for files using command-line tools like grep and egrep Use regular expressions in programming languages like JavaScript, Java, PHP, Python, Microsoft .NET, and C#, as well as in DBMSs including MySQL and Oracle Work with phone numbers, postal codes, social security numbers, IP addresses, URLs, email addresses, and credit card numbers Contents at a Glance 1 Introducing Regular Expressions 2 Matching Single Characters 3 Matching Sets of Characters 4 Using Metacharacters 5 Repeating Matches 6 Position Matching 7 Using Subexpressions 8 Using Backreferences 9 Looking Ahead and Behind 10 Embedding Conditions 11 Regular Expression Solutions to Common Problems Appendix A Regular Expressions in Popular Applications and Languages
Download or read book UML 2 and the Unified Process written by Jim Arlow and published by Pearson Education. This book was released on 2005-06-27 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book manages to convey the practical use of UML 2 in clear and understandable terms with many examples and guidelines. Even for people not working with the Unified Process, the book is still of great use. UML 2 and the Unified Process, Second Edition is a must-read for every UML 2 beginner and a helpful guide and reference for the experienced practitioner." --Roland Leibundgut, Technical Director, Zuehlke Engineering Ltd. "This book is a good starting point for organizations and individuals who are adopting UP and need to understand how to provide visualization of the different aspects needed to satisfy it. " --Eric Naiburg, Market Manager, Desktop Products, IBM Rational Software This thoroughly revised edition provides an indispensable and practical guide to the complex process of object-oriented analysis and design using UML 2. It describes how the process of OO analysis and design fits into the software development lifecycle as defined by the Unified Process (UP). UML 2 and the Unified Process contains a wealth of practical, powerful, and useful techniques that you can apply immediately. As you progress through the text, you will learn OO analysis and design techniques, UML syntax and semantics, and the relevant aspects of the UP. The book provides you with an accurate and succinct summary of both UML and UP from the point of view of the OO analyst and designer. This book provides Chapter roadmaps, detailed diagrams, and margin notes allowing you to focus on your needs Outline summaries for each chapter, making it ideal for revision, and a comprehensive index that can be used as a reference New to this edition: Completely revised and updated for UML 2 syntax Easy to understand explanations of the new UML 2 semantics More real-world examples A new section on the Object Constraint Language (OCL) Introductory material on the OMG's Model Driven Architecture (MDA) The accompanying website provides A complete example of a simple e-commerce system Open source tools for requirements engineering and use case modeling Industrial-strength UML course materials based on the book
Download or read book R for Everyone written by Jared P. Lander and published by Addison-Wesley Professional. This book was released on 2017-06-13 with total page 1456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone, Second Edition, is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, manipulation, and visualization; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques. After all this you’ll make your code reproducible with LaTeX, RMarkdown, and Shiny. By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most. Coverage includes Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using R’s facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalized linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyze univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Register your product at informit.com/register for convenient access to downloads, updates, and corrections as they become available.
