| codeup llc data science: Practical Data Science with R Nina Zumel, John Mount, 2014-04-10 Summary Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Business analysts and developers are increasingly collecting, curating, analyzing, and reporting on crucial business data. The R language and its associated tools provide a straightforward way to tackle day-to-day data science tasks without a lot of academic theory or advanced mathematics. Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations. Using examples from marketing, business intelligence, and decision support, it shows you how to design experiments (such as A/B tests), build predictive models, and present results to audiences of all levels. This book is accessible to readers without a background in data science. Some familiarity with basic statistics, R, or another scripting language is assumed. What's Inside Data science for the business professional Statistical analysis using the R language Project lifecycle, from planning to delivery Numerous instantly familiar use cases Keys to effective data presentations About the Authors Nina Zumel and John Mount are cofounders of a San Francisco-based data science consulting firm. Both hold PhDs from Carnegie Mellon and blog on statistics, probability, and computer science at win-vector.com. Table of Contents PART 1 INTRODUCTION TO DATA SCIENCE The data science process Loading data into R Exploring data Managing data PART 2 MODELING METHODS Choosing and evaluating models Memorization methods Linear and logistic regression Unsupervised methods Exploring advanced methods PART 3 DELIVERING RESULTS Documentation and deployment Producing effective presentations |
| codeup llc data science: Data Mining with Rattle and R Graham Williams, 2011-08-04 Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings. |
| codeup llc data science: Introduction to Scientific Programming and Simulation Using R Owen Jones, Robert Maillardet, Andrew Robinson, 2014-06-12 Learn How to Program Stochastic ModelsHighly recommended, the best-selling first edition of Introduction to Scientific Programming and Simulation Using R was lauded as an excellent, easy-to-read introduction with extensive examples and exercises. This second edition continues to introduce scientific programming and stochastic modelling in a clear, |
| codeup llc data science: Data Parallel C++ James Reinders, Ben Ashbaugh, James Brodman, Michael Kinsner, John Pennycook, Xinmin Tian, 2020-11-19 Learn how to accelerate C++ programs using data parallelism. This open access book enables C++ programmers to be at the forefront of this exciting and important new development that is helping to push computing to new levels. It is full of practical advice, detailed explanations, and code examples to illustrate key topics. Data parallelism in C++ enables access to parallel resources in a modern heterogeneous system, freeing you from being locked into any particular computing device. Now a single C++ application can use any combination of devices—including GPUs, CPUs, FPGAs and AI ASICs—that are suitable to the problems at hand. This book begins by introducing data parallelism and foundational topics for effective use of the SYCL standard from the Khronos Group and Data Parallel C++ (DPC++), the open source compiler used in this book. Later chapters cover advanced topics including error handling, hardware-specific programming, communication and synchronization, and memory model considerations. Data Parallel C++ provides you with everything needed to use SYCL for programming heterogeneous systems. What You'll Learn Accelerate C++ programs using data-parallel programming Target multiple device types (e.g. CPU, GPU, FPGA) Use SYCL and SYCL compilers Connect with computing’s heterogeneous future via Intel’s oneAPI initiative Who This Book Is For Those new data-parallel programming and computer programmers interested in data-parallel programming using C++. |
| codeup llc data science: Practical Deep Learning for Cloud, Mobile, and Edge Anirudh Koul, Siddha Ganju, Meher Kasam, 2019-10-14 Whether you’re a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach. Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite Develop AI for a range of devices including Raspberry Pi, Jetson Nano, and Google Coral Explore fun projects, from Silicon Valley’s Not Hotdog app to 40+ industry case studies Simulate an autonomous car in a video game environment and build a miniature version with reinforcement learning Use transfer learning to train models in minutes Discover 50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users |
| codeup llc data science: Introduction to Python for Engineers and Scientists Sandeep Nagar, 2017-12-06 Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll Learn Understand the fundamentals of the Python programming language Apply Python to numerical computational programming projects in engineering and science Discover the Pythonic way of life Apply data types, operators, and arrays Carry out plotting for visualization Work with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required. |
| codeup llc data science: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| codeup llc data science: Pro Functional PHP Programming Rob Aley, 2017-09-27 Bring the power of functional programming to your PHP applications. From performance optimizations to concurrency, improved testability to code brevity, functional programming has a host of benefits when compared to traditional imperative programming. Part one of Pro Functional PHP Programming takes you through the basics of functional programming, outlining the key concepts and how they translate into standard PHP functions and code. Part two takes this theory and shows you the strategies for implementing it to solve real problems in your new or existing PHP applications. Functional programming is popular in languages such as Lisp, Scheme and Clojure, but PHP also contains all you need to write functional code. This book will show you how to take advantage of functional programming in your own projects, utilizing the PHP programming language that you already know. What You'll Learn Discover functional programming in PHP Work with functional programming functions Design strategies for high-performance applications Manage business logic with functions Use functional programming in object-oriented and procedural applications Employ helper libraries in your application Process big data with functional PHP Who This Book Is For Programmers and web developers with experience of PHP who are looking to get more out of their PHP coding and be able to do more with PHP. |
| codeup llc data science: The Art of Readable Code Dustin Boswell, Trevor Foucher, 2011-11-03 Chapter 5. Knowing What to Comment; What NOT to Comment; Don't Comment Just for the Sake of Commenting; Don't Comment Bad Names--Fix the Names Instead; Recording Your Thoughts; Include Director Commentary; Comment the Flaws in Your Code; Comment on Your Constants; Put Yourself in the Reader's Shoes; Anticipating Likely Questions; Advertising Likely Pitfalls; Big Picture Comments; Summary Comments; Final Thoughts--Getting Over Writer's Block; Summary; Chapter 6. Making Comments Precise and Compact; Keep Comments Compact; Avoid Ambiguous Pronouns; Polish Sloppy Sentences. |
| codeup llc data science: Foundations of Deep Reinforcement Learning Laura Graesser, Wah Loon Keng, 2019-11-20 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. |
| codeup llc data science: Reproducible Finance with R Jonathan K. Regenstein, Jr., 2018-09-24 Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis is a unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples. The book begins with the first step in data science: importing and wrangling data, which in the investment context means importing asset prices, converting to returns, and constructing a portfolio. The next section covers risk and tackles descriptive statistics such as standard deviation, skewness, kurtosis, and their rolling histories. The third section focuses on portfolio theory, analyzing the Sharpe Ratio, CAPM, and Fama French models. The book concludes with applications for finding individual asset contribution to risk and for running Monte Carlo simulations. For each of these tasks, the three major coding paradigms are explored and the work is wrapped into interactive Shiny dashboards. |
| codeup llc data science: Machine Learning with R Brett Lantz, 2013-10-25 Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required. |
| codeup llc data science: Mastering Enterprise JavaBeans Ed Roman, Rima Patel Sriganesh, Gerald Brose, 2004-12-22 Includes more than 30 percent revised material and five new chapters, covering the new 2.1 features such as EJB Timer Service and JMS as well as the latest open source Java solutions The book was developed as part of TheServerSide.com online EJB community, ensuring a built-in audience Demonstrates how to build an EJB system, program with EJB, adopt best practices, and harness advanced EJB concepts and techniques, including transactions, persistence, clustering, integration, and performance optimization Offers practical guidance on when not to use EJB and how to use simpler, less costly open source technologies in place of or in conjunction with EJB |
| codeup llc data science: Deep Learning from Scratch Seth Weidman, 2019-09-09 With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. You’ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You’ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, you’ll be set up for success on all future deep learning projects. This book provides: Extremely clear and thorough mental models—accompanied by working code examples and mathematical explanations—for understanding neural networks Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework Working implementations and clear-cut explanations of convolutional and recurrent neural networks Implementation of these neural network concepts using the popular PyTorch framework |
| codeup llc data science: AI and Machine Learning for On-Device Development Laurence Moroney, 2021-08-12 Chapter 2. Introduction to Computer Vision -- Using Neurons for Vision -- Your First Classifier: Recognizing Clothing Items -- The Data: Fashion MNIST -- A Model Architecture to Parse Fashion MNIST -- Coding the Fashion MNIST Model -- Transfer Learning for Computer Vision -- Summary -- Chapter 3. Introduction to ML Kit -- Building a Face Detection App on Android -- Step 1: Create the App with Android Studio -- Step 2: Add and Configure ML Kit -- Step 3: Define the User Interface -- Step 4: Add the Images as Assets -- Step 5: Load the UI with a Default Picture. |
| codeup llc data science: Multivariable Calculus James Stewart, 2011-09-27 Success in your calculus course starts here! James Stewart's CALCULUS, 7e, International Metric texts are world-wide best-sellers for a reason: they are clear, accurate, and filled with relevant, real-world examples. With MULTIVARIABLE CALCULUS, 7e, International Metric Edition Stewart conveys not only the utility of calculus to help you develop technical competence, but also gives you an appreciation for the intrinsic beauty of the subject. His patient examples and built-in learning aids will help you build your mathematical confidence and achieve your goals in the course! |
| codeup llc data science: A Beginner's Guide to Discrete Mathematics W.D. Wallis, 2011-10-07 Wallis's book on discrete mathematics is a resource for an introductory course in a subject fundamental to both mathematics and computer science, a course that is expected not only to cover certain specific topics but also to introduce students to important modes of thought specific to each discipline . . . Lower-division undergraduates through graduate students. —Choice reviews (Review of the First Edition) Very appropriately entitled as a 'beginner's guide', this textbook presents itself as the first exposure to discrete mathematics and rigorous proof for the mathematics or computer science student. —Zentralblatt Math (Review of the First Edition) This second edition of A Beginner’s Guide to Discrete Mathematics presents a detailed guide to discrete mathematics and its relationship to other mathematical subjects including set theory, probability, cryptography, graph theory, and number theory. This textbook has a distinctly applied orientation and explores a variety of applications. Key Features of the second edition: * Includes a new chapter on the theory of voting as well as numerous new examples and exercises throughout the book * Introduces functions, vectors, matrices, number systems, scientific notations, and the representation of numbers in computers * Provides examples which then lead into easy practice problems throughout the text and full exercise at the end of each chapter * Full solutions for practice problems are provided at the end of the book This text is intended for undergraduates in mathematics and computer science, however, featured special topics and applications may also interest graduate students. |
| codeup llc data science: Surrogates Robert B. Gramacy, 2020-03-10 Computer simulation experiments are essential to modern scientific discovery, whether that be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are meta-models of computer simulations, used to solve mathematical models that are too intricate to be worked by hand. Gaussian process (GP) regression is a supremely flexible tool for the analysis of computer simulation experiments. This book presents an applied introduction to GP regression for modelling and optimization of computer simulation experiments. Features: • Emphasis on methods, applications, and reproducibility. • R code is integrated throughout for application of the methods. • Includes more than 200 full colour figures. • Includes many exercises to supplement understanding, with separate solutions available from the author. • Supported by a website with full code available to reproduce all methods and examples. The book is primarily designed as a textbook for postgraduate students studying GP regression from mathematics, statistics, computer science, and engineering. Given the breadth of examples, it could also be used by researchers from these fields, as well as from economics, life science, social science, etc. |
| codeup llc data science: Digital Information and Communication Technology and Its Applications Hocine Cherifi, Jasni Mohamad Zain, Eyas El-Qawasmeh, 2011-06-17 This two-volume set CCIS 166 and 167 constitutes the refereed proceedings of the International Conference on Digital Information and Communication Technology and its Applications, DICTAP 2011, held in Dijon, France, in June 2010. The 128 revised full papers presented in both volumes were carefully reviewed and selected from 330 submissions. The papers are organized in topical sections on Web applications; image processing; visual interfaces and user experience; network security; ad hoc network; cloud computing; Data Compression; Software Engineering; Networking and Mobiles; Distributed and Parallel processing; social networks; ontology; algorithms; multimedia; e-learning; interactive environments and emergent technologies for e-learning; signal processing; information and data management. |
| codeup llc data science: Health Informatics: Practical Guide for Healthcare and Information Technology Professionals (Sixth Edition) Robert E. Hoyt, Ann K. Yoshihashi, 2014 Health Informatics (HI) focuses on the application of Information Technology (IT) to the field of medicine to improve individual and population healthcare delivery, education and research. This extensively updated fifth edition reflects the current knowledge in Health Informatics and provides learning objectives, key points, case studies and references. |
| codeup llc data science: Well Completion Design Jonathan Bellarby, 2009-04-13 Completions are the conduit between hydrocarbon reservoirs and surface facilities. They are a fundamental part of any hydrocarbon field development project. The have to be designed for safely maximising the hydrocarbon recovery from the well and may have to last for many years under ever changing conditions. Issues include: connection with the reservoir rock, avoiding sand production, selecting the correct interval, pumps and other forms of artificial lift, safety and integrity, equipment selection and installation and future well interventions. - Course book based on course well completion design by TRACS International - Unique in its field: Coverage of offshore, subsea, and landbased completions in all of the major hydrocarbon basins of the world - Full colour |
| codeup llc data science: The Viral Storm Nathan D. Wolfe, 2011-10-27 'Wolfe has an important story to tell and as a virologist at the forefront of pandemic forecasting, he is the perfect person to tell it' Guardian In The Viral Storm award-winning biologist Nathan Wolfe - known as 'the Indiana Jones of virus hunters' for his work in jungles and rain forests across the world - shows why we are so vulnerable to a global pandemic. The Viral Storm examines how viruses like HIV, swine flu, and bird flu have almost wiped us out in the past - and may do so in the future. It explores why modern life makes us so at risk to global pandemics, and what new technologies can do to prevent them. Wolfe's provocative vision may leave you feeling distinctly uncomfortable - but it will reveal exactly what it is we are up against. 'An excellent piece of scientific gothic, rich in descriptions of the threat we face from emerging viruses' Nature 'Part autobiography, part warning ... enthralling' BBC Focus 'Quietly terrifying ... It's hard not to feel a bit feverish at times while reading' Boston Globe 'The plague-ridden future imagined by this authoritative, measured, yet gripping book is extremely alarming' Sunday Times 'Nathan Wolfe is saving the world from near-inevitable pandemic ... a kick-ass book' Mary Roach, author of Stiff 'The world's most prominent virus hunter' New Yorker 'A good place to start preparing for what might come' New Humanist |
| codeup llc data science: Robust Python Patrick Viafore, 2021-07-12 Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes. In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code. Learn why types are essential in modern development ecosystems Understand how type choices such as classes, dictionaries, and enums reflect specific intents Make Python extensible for the future without adding bloat Use popular Python tools to increase the safety and robustness of your codebase Evaluate current code to detect common maintainability gotchas Build a safety net around your codebase with linters and tests |
| codeup llc data science: Learning Perl Objects, References, and Modules Randal L. Schwartz, Tom Phoenix, 2003-06-09 Perl is a versatile, powerful programming language used in a variety of disciplines, ranging from system administration to web programming to database manipulation. One slogan of Perl is that it makes easy things easy and hard things possible. This book is about making the leap from the easy things to the hard ones.Learning Perl Objects, References & Modules offers a gentle but thorough introduction to advanced programming in Perl. Written by the authors of the best-selling Learning Perl, this book picks up where that book left off. Topics include: Packages and namespaces References and scoping Manipulating complex data structures Object-oriented programming Writing and using modules Contributing to CPAN Following the successful format of Learning Perl, each chapter in the book is designed to be small enough to be read in just an hour or two, ending with a series of exercises to help you practice what you've learned. To use the book, you just need to be familiar with the material in Learning Perl and have ambition to go further.Perl is a different language to different people. It is a quick scripting tool for some, and a fully-featured object-oriented language for others. It is used for everything from performing quick global replacements on text files, to crunching huge, complex sets of scientific data that take weeks to process. Perl is what you make of it. But regardless of what you use Perl for, this book helps you do it more effectively, efficiently, and elegantly.Learning Perl Objects, References & Modules is about learning to use Perl as a programming language, and not just a scripting language. This is the book that separates the Perl dabbler from the Perl programmer. |
| codeup llc data science: Algorithms for Optimization Mykel J. Kochenderfer, Tim A. Wheeler, 2019-03-12 A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals. |
| codeup llc data science: Modern C++ Programming with Test-Driven Development Jeff Langr, 2013-10-10 If you program in C++ you've been neglected. Test-driven development (TDD) is a modern software development practice that can dramatically reduce the number of defects in systems, produce more maintainable code, and give you the confidence to change your software to meet changing needs. But C++ programmers have been ignored by those promoting TDD--until now. In this book, Jeff Langr gives you hands-on lessons in the challenges and rewards of doing TDD in C++. Modern C++ Programming With Test-Driven Development, the only comprehensive treatment on TDD in C++ provides you with everything you need to know about TDD, and the challenges and benefits of implementing it in your C++ systems. Its many detailed code examples take you step-by-step from TDD basics to advanced concepts. As a veteran C++ programmer, you're already writing high-quality code, and you work hard to maintain code quality. It doesn't have to be that hard. In this book, you'll learn: how to use TDD to improve legacy C++ systems how to identify and deal with troublesome system dependencies how to do dependency injection, which is particularly tricky in C++ how to use testing tools for C++ that aid TDD new C++11 features that facilitate TDD As you grow in TDD mastery, you'll discover how to keep a massive C++ system from becoming a design mess over time, as well as particular C++ trouble spots to avoid. You'll find out how to prevent your tests from being a maintenance burden and how to think in TDD without giving up your hard-won C++ skills. Finally, you'll see how to grow and sustain TDD in your team. Whether you're a complete unit-testing novice or an experienced tester, this book will lead you to mastery of test-driven development in C++. What You Need A C++ compiler running under Windows or Linux, preferably one that supports C++11. Examples presented in the book were built under gcc 4.7.2. Google Mock 1.6 (downloadable for free; it contains Google Test as well) or an alternate C++ unit testing tool. Most examples in the book are written for Google Mock, but it isn't difficult to translate them to your tool of choice. A good programmer's editor or IDE. cmake, preferably. Of course, you can use your own preferred make too. CMakeLists.txt files are provided for each project. Examples provided were built using cmake version 2.8.9. Various freely-available third-party libraries are used as the basis for examples in the book. These include: cURL JsonCpp Boost (filesystem, date_time/gregorian, algorithm, assign) Several examples use the boost headers/libraries. Only one example uses cURL and JsonCpp. |
| codeup llc data science: Hack the Stack Stephen Watkins, George Mays, Ronald M. Bandes, Brandon Franklin, Michael Gregg, Chris Ries, 2006-11-06 This book looks at network security in a new and refreshing way. It guides readers step-by-step through the stack -- the seven layers of a network. Each chapter focuses on one layer of the stack along with the attacks, vulnerabilities, and exploits that can be found at that layer. The book even includes a chapter on the mythical eighth layer: The people layer. This book is designed to offer readers a deeper understanding of many common vulnerabilities and the ways in which attacker's exploit, manipulate, misuse, and abuse protocols and applications. The authors guide the readers through this process by using tools such as Ethereal (sniffer) and Snort (IDS). The sniffer is used to help readers understand how the protocols should work and what the various attacks are doing to break them. IDS is used to demonstrate the format of specific signatures and provide the reader with the skills needed to recognize and detect attacks when they occur. What makes this book unique is that it presents the material in a layer by layer approach which offers the readers a way to learn about exploits in a manner similar to which they most likely originally learned networking. This methodology makes this book a useful tool to not only security professionals but also for networking professionals, application programmers, and others. All of the primary protocols such as IP, ICMP, TCP are discussed but each from a security perspective. The authors convey the mindset of the attacker by examining how seemingly small flaws are often the catalyst of potential threats. The book considers the general kinds of things that may be monitored that would have alerted users of an attack.* Remember being a child and wanting to take something apart, like a phone, to see how it worked? This book is for you then as it details how specific hacker tools and techniques accomplish the things they do. * This book will not only give you knowledge of security tools but will provide you the ability to design more robust security solutions * Anyone can tell you what a tool does but this book shows you how the tool works |
| codeup llc data science: Beginning Blockchain Bikramaditya Singhal, Gautam Dhameja, Priyansu Sekhar Panda, 2018-07-06 Understand the nuts and bolts of Blockchain, its different flavors with simple use cases, and cryptographic fundamentals. You will also learn some design considerations that can help you build custom solutions. Beginning Blockchain is a beginner’s guide to understanding the core concepts of Blockchain from a technical perspective. By learning the design constructs of different types of Blockchain, you will get a better understanding of building the best solution for specific use cases. The book covers the technical aspects of Blockchain technologies, cryptography, cryptocurrencies, and distributed consensus mechanisms. You will learn how these systems work and how to engineer them to design next-gen business solutions. What You'll Learn Get a detailed look at how cryptocurrencies work Understand the core technical components of Blockchain Build a secured Blockchain solution from cryptographic primitives Discover how to use different Blockchain platforms and their suitable use cases Know the current development status, scope, limitations, and future of Blockchain Who This Book Is For Software developers and architects, computer science graduates, entrepreneurs, and anyone wishing to dive deeper into blockchain fundamentals. A basic understanding of computer science, data structure, and algorithms is helpful. |
| codeup llc data science: Graphics Programming in Icon Ralph E. Griswold, Clinton L. Jeffery, Gregg M. Townsend, 1998 No publisher description provided for this product. |
| codeup llc data science: Connectome Sebastian Seung, 2012-02-07 “Accessible, witty . . . an important new researcher, philosopher and popularizer of brain science . . . on par with cosmology’s Brian Greene and the late Carl Sagan” (The Plain Dealer). One of the Wall Street Journal’s 10 Best Nonfiction Books of the Year and a Publishers Weekly “Top Ten in Science” Title Every person is unique, but science has struggled to pinpoint where, precisely, that uniqueness resides. Our genome may determine our eye color and even aspects of our character. But our friendships, failures, and passions also shape who we are. The question is: How? Sebastian Seung is at the forefront of a revolution in neuroscience. He believes that our identity lies not in our genes, but in the connections between our brain cells—our particular wiring. Seung and a dedicated group of researchers are leading the effort to map these connections, neuron by neuron, synapse by synapse. It’s a monumental effort, but if they succeed, they will uncover the basis of personality, identity, intelligence, memory, and perhaps disorders such as autism and schizophrenia. Connectome is a mind-bending adventure story offering a daring scientific and technological vision for understanding what makes us who we are, as individuals and as a species. “This is complicated stuff, and it is a testament to Dr. Seung’s remarkable clarity of exposition that the reader is swept along with his enthusiasm, as he moves from the basics of neuroscience out to the farthest regions of the hypothetical, sketching out a spectacularly illustrated giant map of the universe of man.” —TheNew York Times “An elegant primer on what’s known about how the brain is organized and how it grows, wires its neurons, perceives its environment, modifies or repairs itself, and stores information. Seung is a clear, lively writer who chooses vivid examples.” —TheWashington Post |
| codeup llc data science: Introductory Techniques for 3-D Computer Vision Emanuele Trucco, Alessandro Verri, 1998 This text provides readers with a starting point to understand and investigate the literature of computer vision, listing conferences, journals and Internet sites. |
| codeup llc data science: Statistical Computing in C++ and R Randall L. Eubank, Ana Kupresanin, 2011-12-01 With the advancement of statistical methodology inextricably linked to the use of computers, new methodological ideas must be translated into usable code and then numerically evaluated relative to competing procedures. In response to this, Statistical Computing in C++ and R concentrates on the writing of code rather than the development and study of numerical algorithms per se. The book discusses code development in C++ and R and the use of these symbiotic languages in unison. It emphasizes that each offers distinct features that, when used in tandem, can take code writing beyond what can be obtained from either language alone. The text begins with some basics of object-oriented languages, followed by a boot-camp on the use of C++ and R. The authors then discuss code development for the solution of specific computational problems that are relevant to statistics including optimization, numerical linear algebra, and random number generation. Later chapters introduce abstract data structures (ADTs) and parallel computing concepts. The appendices cover R and UNIX Shell programming. Features Includes numerous student exercises ranging from elementary to challenging Integrates both C++ and R for the solution of statistical computing problems Uses C++ code in R and R functions in C++ programs Provides downloadable programs, available from the authors’ website The translation of a mathematical problem into its computational analog (or analogs) is a skill that must be learned, like any other, by actively solving relevant problems. The text reveals the basic principles of algorithmic thinking essential to the modern statistician as well as the fundamental skill of communicating with a computer through the use of the computer languages C++ and R. The book lays the foundation for original code development in a research environment. |
| codeup llc data science: Network World , 1995-05-15 For more than 20 years, Network World has been the premier provider of information, intelligence and insight for network and IT executives responsible for the digital nervous systems of large organizations. Readers are responsible for designing, implementing and managing the voice, data and video systems their companies use to support everything from business critical applications to employee collaboration and electronic commerce. |
| codeup llc data science: Swift Programming Matthew Mathias, John Gallagher, 2016-11-23 This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Through the authors' carefully constructed explanations and examples, you will develop an understanding of Swift grammar and the elements of effective Swift style. This book is written for Swift 3.0 and will also show you how to navigate Xcode 8 and get the most out of Apple's documentation. Throughout the book, the authors share their insights into Swift to ensure that you understand the hows and whys of Swift and can put that understanding to use in different contexts. After working through the book, you will have the knowledge and confidence to develop your own solutions to a wide range of programming challenges using Swift. |
| codeup llc data science: Deep Learning Illustrated Jon Krohn, Grant Beyleveld, Aglaé Bassens, 2019-08-05 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. |
| codeup llc data science: Python for Teenagers James R. Payne, 2019-05-07 Discover everything you need to know about Python to turn your passion of programming into a job you'll love. Fueled by fun and practical examples, this book gives high schoolers who want learn an easy programming language ideas for how to leverage them in the workforce. Start with the basics and before you know it, you'll be building your own web sites, doing white-hat hacking, finding code bugs and errors, and creating games, including using Python to roll characters for RPGs. Every chapter is relaxed and informal, like learning with a cool teacher all the time. Computers, phones and the web are your playground, and you'll be ready to join the party with your own content. Going beyond posts and uploads means learning to program, and Python is a great choice to get started. It's quick to learn, it's flexible, and if you want, it may get you a Python job that pays more than minimum wage when you're out of school. Python for Teenagers is the most fun you'll have while learning. What You'll Learn Review programming basics - you gotta start somewhere Code applications that follow directions and make decisions Understand Classes and objects - when a program is a child Make games with graphics and animationWho This Book Is For High schoolers who want learn an easy programming language. |
| codeup llc data science: BigQuery for Data Warehousing Mark Mucchetti, 2020-12-20 Create a data warehouse, complete with reporting and dashboards using Google’s BigQuery technology. This book takes you from the basic concepts of data warehousing through the design, build, load, and maintenance phases. You will build capabilities to capture data from the operational environment, and then mine and analyze that data for insight into making your business more successful. You will gain practical knowledge about how to use BigQuery to solve data challenges in your organization. BigQuery is a managed cloud platform from Google that provides enterprise data warehousing and reporting capabilities. Part I of this book shows you how to design and provision a data warehouse in the BigQuery platform. Part II teaches you how to load and stream your operational data into the warehouse to make it ready for analysis and reporting. Parts III and IV cover querying and maintaining, helping you keep your information relevant with other Google Cloud Platform services and advanced BigQuery. Part V takes reporting to the next level by showing you how to create dashboards to provide at-a-glance visual representations of your business situation. Part VI provides an introduction to data science with BigQuery, covering machine learning and Jupyter notebooks. What You Will Learn Design a data warehouse for your project or organization Load data from a variety of external and internal sources Integrate other Google Cloud Platform services for more complex workflows Maintain and scale your data warehouse as your organization grows Analyze, report, and create dashboards on the information in the warehouse Become familiar with machine learning techniques using BigQuery ML Who This Book Is For Developers who want to provide business users with fast, reliable, and insightful analysis from operational data, and data analysts interested in a cloud-based solution that avoids the pain of provisioning their own servers. |
| codeup llc data science: The Effective Engineer Edmond Lau, 2015-03-19 Introducing The Effective Engineer--the only book designed specifically for today's software engineers, based on extensive interviews with engineering leaders at top tech companies, and packed with hundreds of techniques to accelerate your career. |
| codeup llc data science: Machine Learning with R Brett Lantz, 2015-07-31 Updated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering. |
| codeup llc data science: Machine Learning for Subsurface Characterization Siddharth Misra, Hao Li, Jiabo He, 2019-10-12 Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the big data. Deep learning (DL) is a subset of machine learning that processes big data to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. - Learn from 13 practical case studies using field, laboratory, and simulation data - Become knowledgeable with data science and analytics terminology relevant to subsurface characterization - Learn frameworks, concepts, and methods important for the engineer's and geoscientist's toolbox needed to support |
Best Tequila Jello Shots Recipe » Recipefairy.com
Aug 6, 2023 · Prepare to shake up your next party with the ultimate crowd-pleaser: Tequila Jello Shots. These vibrant, zesty, and fun-filled goodies offer a unique twist to the traditional shot, …
Best Tequila JELLO Shots - Food Lovin Family
May 17, 2021 · Learn how to make tequila JELLO shots quick and easy with JELLO and margarita mix.
17 Best Tequila Jello Shots - Outside The Wine Box
Mix things up at your next party with these easy and delicious tequila jello shots! From flavors like the classic margarita to spicy Mexican candy, these jello shots are a lighthearted but …
Simple Tequila Lime Jello Shots - Simplistically Living
May 23, 2025 · These Tequila Lime Jello Shots are guaranteed to be a party favorite! Bursting with zesty lime flavor and a bold tequila kick, they’re easy to make with just 3 simple ingredients.
How to make jello shots with tequila? - Chef's Resource
Jello shots are a fun and colorful way to serve alcohol at parties and gatherings. By combining the sweet and fruity flavors of Jello with the vibrant kick of tequila, you can create a unique and …
20 Jello Shots With Tequila - Oh My Creative
Dec 17, 2023 · These delightful jello shots capture the essence of the classic Tequila Sunrise cocktail, complete with layers of orange juice, grenadine, and tequila. They're not only visually …
Tequila Sunrise Jello Shots - The Chunky Chef
Mar 6, 2023 · Sweet and fruity, these Tequila Sunrise Jello Shots are a fun way to enjoy a classic summer cocktail… in Jello shot form! Perfect for any summer party, you’ll love how easy these …
Margarita Jello Shots Recipe (Made With Tequila)
May 22, 2024 · This Margarita Jello Shots recipe made with Tequila is a fun take on the cocktail with the same name. A boozy treat for any adult party!
Tequila Sunrise Jello Shots - Love Bakes Good Cakes
May 13, 2025 · Tequila Sunrise Jello Shots are a fun, colorful treat with vibrant cherry and orange layers. Perfect for parties and easy to make! Arrange 30 (2-ounce) shot cups on a tray. In a …
Margarita Jell-O® Shots Recipe
Jun 3, 2025 · Gather all ingredients. Combine boiling water and gelatin mix in a bowl. Stir to dissolve. Add tequila, triple sec, and water. Stir again. Pour mixture into shot glasses. …
Solo High Arched Feet Porn Videos | Pornhub.com
Watch Solo High Arched Feet porn videos for free, here on Pornhub.com. Discover the growing collection of high quality Most Relevant XXX movies and clips. No other sex tube is more …
Free High Arched Feet Porn | PornKai.com
High Arched Feet Porn Videos: WATCH FREE here!
'arched feet solo' Search - XNXX.COM
Foot job filly, Kendra Heart slides her high arched feet all over a throbbing dick, milking those blue balls until she gets that hot cum on her ! Full Video & More Girls @ FuckedFeet.com!
'solo sexy high arch feet' Search - XVIDEOS.COM
61,144 solo sexy high arch feet FREE videos found on XVIDEOS for this search.
High Arched Feet Playlist - HD Porn Videos - SpankBang
Watch High Arched Feet playlist for free on SpankBang - 91 movies and sexy clips. Play trending and hottest High Arched Feet movies.
Free High Arched Feet Porn Videos (220) - Tubesafari.com
Free high arched feet porn: 220 videos. WATCH NOW for FREE!
'arched feet solo' Search - XVIDEOS.COM
23,434 arched feet solo FREE videos found on XVIDEOS for this search.
Cam Girl with Super High Arches: Webcam Porn | xHamster
Watch Cam Girl with Super High Arches video on xHamster, the biggest sex tube site with tons of free Fetish Webcam & Foot Fetish porn movies!
Sexy High Arched Feet Porn Videos - SpankBang
Watch Sexy High Arched Feet porn videos here on SpankBang! Explore our steamy collection of free adult videos and enjoy unlimited high-quality scenes.
Sexy High Arched Feet Porn Videos | Pornhub.com
Watch Sexy High Arched Feet porn videos for free, here on Pornhub.com. Discover the growing collection of high quality Most Relevant XXX movies and clips. No other sex tube is more …
