Advertisement
| data science with c++: C++ and Data Analytics 2 Bundle Manuscript Essential Beginners Guide on Enriching Your C++ Programming Skills and Learn Practical Data Analytics, Data Science, and Predictive Analytics for Beginners Isaac D. Cody, 2017-03-07 This is a 2 book bundle related to C++ programming and Data Analytics! Two manuscripts for the price of one! Whats included in this 2 book bundle manuscript: C++: Learn C++ Like a Boss. A Beginners Guide in Coding Programming And Dominating C++. Novice to Expert Guide To Learn and Master C++ Fast Data Analytics: Practical Data Analysis and Statistical Guide to Transform and Evolve Any Business In C++ programming, you will learn the basics about: Compliers, syntax, class, objects, and variables Identifiers, trigraphs, data types, lines, and characters Boolean and functions Arrays, loops, and conditions Various types of operators Decision statements, if else statements Constants and literals Quick follow up quizzes and answers Guided examples and much more! In the Data Analytics portion of this bundle, you will learn: Why your business should be using data analytics Issues with using big data Effective data management Examples of data management in the real-world The different kinds of data analytics and their definitions How data management, data mining, data integration and data warehousing work together A step-by-step guide for conducting data analysis for your business An organizational guide to data analytics Tools for data visualization Get your copy today! Scroll up and learn how to program in both C++ and Data Analytics! |
| data science with c++: Parallel Computing for Data Science Norman Matloff, 2015-06-04 This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic n observations, p variables matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming. |
| data science with c++: Mastering Algorithms with C Kyle Loudon, 1999 Implementations, as well as interesting, real-world examples of each data structure and algorithm, are shown in the text. Full source code appears on the accompanying disk. |
| data science with c++: Hands-On Machine Learning with C++ Kirill Kolodiazhnyi, 2020-05-15 Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets Key FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook Description C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. What you will learnExplore how to load and preprocess various data types to suitable C++ data structuresEmploy key machine learning algorithms with various C++ librariesUnderstand the grid-search approach to find the best parameters for a machine learning modelImplement an algorithm for filtering anomalies in user data using Gaussian distributionImprove collaborative filtering to deal with dynamic user preferencesUse C++ libraries and APIs to manage model structures and parametersImplement a C++ program to solve image classification tasks with LeNet architectureWho this book is for You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book. |
| data science with c++: Data Structures and Algorithm Analysis in C+ Mark Allen Weiss, 2003 In this second edition of his successful book, experienced teacher and author Mark Allen Weiss continues to refine and enhance his innovative approach to algorithms and data structures. Written for the advanced data structures course, this text highlights theoretical topics such as abstract data types and the efficiency of algorithms, as well as performance and running time. Before covering algorithms and data structures, the author provides a brief introduction to C++ for programmers unfamiliar with the language. Dr Weiss's clear writing style, logical organization of topics, and extensive use of figures and examples to demonstrate the successive stages of an algorithm make this an accessible, valuable text. New to this Edition *An appendix on the Standard Template Library (STL) *C++ code, tested on multiple platforms, that conforms to the ANSI ISO final draft standard 0201361221B04062001 |
| data science with c++: C++ Plus Data Structures Nell B. Dale, 2003 Computer Science |
| data science with c++: Guide to Scientific Computing in C++ Joe Pitt-Francis, Jonathan Whiteley, 2012-02-15 This easy-to-read textbook/reference presents an essential guide to object-oriented C++ programming for scientific computing. With a practical focus on learning by example, the theory is supported by numerous exercises. Features: provides a specific focus on the application of C++ to scientific computing, including parallel computing using MPI; stresses the importance of a clear programming style to minimize the introduction of errors into code; presents a practical introduction to procedural programming in C++, covering variables, flow of control, input and output, pointers, functions, and reference variables; exhibits the efficacy of classes, highlighting the main features of object-orientation; examines more advanced C++ features, such as templates and exceptions; supplies useful tips and examples throughout the text, together with chapter-ending exercises, and code available to download from Springer. |
| data science with c++: C++ Crash Course Josh Lospinoso, 2019-09-24 A fast-paced, thorough introduction to modern C++ written for experienced programmers. After reading C++ Crash Course, you'll be proficient in the core language concepts, the C++ Standard Library, and the Boost Libraries. C++ is one of the most widely used languages for real-world software. In the hands of a knowledgeable programmer, C++ can produce small, efficient, and readable code that any programmer would be proud of. Designed for intermediate to advanced programmers, C++ Crash Course cuts through the weeds to get you straight to the core of C++17, the most modern revision of the ISO standard. Part 1 covers the core of the C++ language, where you'll learn about everything from types and functions, to the object life cycle and expressions. Part 2 introduces you to the C++ Standard Library and Boost Libraries, where you'll learn about all of the high-quality, fully-featured facilities available to you. You'll cover special utility classes, data structures, and algorithms, and learn how to manipulate file systems and build high-performance programs that communicate over networks. You'll learn all the major features of modern C++, including: Fundamental types, reference types, and user-defined types The object lifecycle including storage duration, memory management, exceptions, call stacks, and the RAII paradigm Compile-time polymorphism with templates and run-time polymorphism with virtual classes Advanced expressions, statements, and functions Smart pointers, data structures, dates and times, numerics, and probability/statistics facilities Containers, iterators, strings, and algorithms Streams and files, concurrency, networking, and application development With well over 500 code samples and nearly 100 exercises, C++ Crash Course is sure to help you build a strong C++ foundation. |
| data science with c++: Data Structures and Algorithm Analysis in C++, Third Edition Clifford A. Shaffer, 2012-07-26 Comprehensive treatment focuses on creation of efficient data structures and algorithms and selection or design of data structure best suited to specific problems. This edition uses C++ as the programming language. |
| data science with c++: A Practical Introduction to Data Structures and Algorithm Analysis Clifford A. Shaffer, 2001 This practical text contains fairly traditional coverage of data structures with a clear and complete use of algorithm analysis, and some emphasis on file processing techniques as relevant to modern programmers. It fully integrates OO programming with these topics, as part of the detailed presentation of OO programming itself.Chapter topics include lists, stacks, and queues; binary and general trees; graphs; file processing and external sorting; searching; indexing; and limits to computation.For programmers who need a good reference on data structures. |
| data science with c++: Algorithms and Data Structures in C++ Alan Parker, 2018-05-11 Algorithms and Data Structures in C++ introduces modern issues in the theory of algorithms, emphasizing complexity, graphs, parallel processing, and visualization. To accomplish this, the book uses an appropriate subset of frequently utilized and representative algorithms and applications in order to demonstrate the unique and modern aspects of the C++ programming language. What makes this book so valuable is that many complete C++ programs have been compiled and executed on multiple platforms. Each program presented is a stand-alone functional program. A number of applications that exercise significant features of C++, including templates and polymorphisms, is included. The book is a perfect text for computer science and engineering students in traditional algorithms or data structures courses. It will also benefit professionals in all fields of computer science and engineering. |
| data science with c++: Data Structures and Algorithm Analysis in C++ Weiss, Weiss Mark Allen, 2007-09 The C++ language is brought up-to-date and simplified, and the Standard Template Library is now fully incorporated throughout the text. Data Structures and Algorithm Analysis in C++ is logically organized to cover advanced data structures topics from binary heaps to sorting to NP-completeness. Figures and examples illustrating successive stages of algorithms contribute to Weiss' careful, rigorous and in-depth analysis of each type of algorithm. |
| data science with c++: Data Structure and Algorithms Using C++ Sachi Nandan Mohanty, Pabitra Kumar Tripathy, 2021-01-12 Everyone knows that programming plays a vital role as a solution to automate and execute a task in a proper manner. Irrespective of mathematical problems, the skills of programming are necessary to solve any type of problems that may be correlated to solve real life problems efficiently and effectively. This book is intended to flow from the basic concepts of C++ to technicalities of the programming language, its approach and debugging. The chapters of the book flow with the formulation of the problem, it's designing, finding the step-by-step solution procedure along with its compilation, debugging and execution with the output. Keeping in mind the learner’s sentiments and requirements, the exemplary programs are narrated with a simple approach so that it can lead to creation of good programs that not only executes properly to give the output, but also enables the learners to incorporate programming skills in them. The style of writing a program using a programming language is also emphasized by introducing the inclusion of comments wherever necessary to encourage writing more readable and well commented programs. As practice makes perfect, each chapter is also enriched with practice exercise questions so as to build the confidence of writing the programs for learners. The book is a complete and all-inclusive handbook of C++ that covers all that a learner as a beginner would expect, as well as complete enough to go ahead with advanced programming. This book will provide a fundamental idea about the concepts of data structures and associated algorithms. By going through the book, the reader will be able to understand about the different types of algorithms and at which situation and what type of algorithms will be applicable. |
| data science with c++: Computer Programming with C++ Kunal Pimparkhede, 2017-01-16 Provides an in-depth explanation of the C and C++ programming languages along with the fundamentals of object oriented programming paradigm-- |
| data science with c++: Modern Data Mining Algorithms in C++ and CUDA C Timothy Masters, 2020-06-30 Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You’ll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov model Improvements on traditional stepwise selection Nominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets. Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts. C++ and CUDA C experience is highly recommended. However, this book can be used as a framework using other languages such as Python. |
| data science with c++: Data Abstraction and Structures Using C++ Mark R. Headington, David D. Riley, 1994 |
| data science with c++: Programming in C++ for Engineering and Science Larry Nyhoff, 2012-08-01 Developed from the author's many years of teaching computing courses, Programming in C++ for Engineering and Science guides students in designing programs to solve real problems encountered in engineering and scientific applications. These problems include radioactive decay, pollution indexes, digital circuits, differential equations, Internet addr |
| data science with c++: Data Structures and Algorithms in C++ Michael T. Goodrich, Roberto Tamassia, David M. Mount, 2011-02-22 An updated, innovative approach to data structures and algorithms Written by an author team of experts in their fields, this authoritative guide demystifies even the most difficult mathematical concepts so that you can gain a clear understanding of data structures and algorithms in C++. The unparalleled author team incorporates the object-oriented design paradigm using C++ as the implementation language, while also providing intuition and analysis of fundamental algorithms. Offers a unique multimedia format for learning the fundamentals of data structures and algorithms Allows you to visualize key analytic concepts, learn about the most recent insights in the field, and do data structure design Provides clear approaches for developing programs Features a clear, easy-to-understand writing style that breaks down even the most difficult mathematical concepts Building on the success of the first edition, this new version offers you an innovative approach to fundamental data structures and algorithms. |
| data science with c++: Problem Solving with Algorithms and Data Structures Using Python Bradley N. Miller, David L. Ranum, 2011 Thes book has three key features : fundamental data structures and algorithms; algorithm analysis in terms of Big-O running time in introducied early and applied throught; pytohn is used to facilitates the success in using and mastering data strucutes and algorithms. |
| data science with c++: Data Mining Algorithms in C++ Timothy Masters, 2017-12-15 Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll Learn Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high Who This Book Is For Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language. |
| data science with c++: Data Structures Using C++ D. S. Malik, 2010 The latest book from Cengage Learning on Data Structures Using C++, International Edition |
| data science with c++: Murach's Python Programming (2nd Edition) Joel Murach, Michael Urban, 2021-04 If you want to learn how to program but dont know where to start, this is the right book and the right language for you. From the first page, our self-paced approach will help you build competence and confidence in your programming skills. And Python is the best language ever for learning how to program because of its simplicity and breadthtwo features that are hard to find in a single language. But this isnt just a book for beginners! Our self-paced approach also works for experienced programmers, helping you learn Python faster and better than youve ever learned a language before. By the time youre through, you will have mastered the key Python skills that are needed on the job, including those for object-oriented, database, and GUI programming. To make all of this possible, section 1 presents an 8-chapter course that will get anyone off to a great start with Python. Section 2 builds on that base by presenting the other essential skills that every Python programmer should have. Section 3 shows you how to develop object-oriented programs, a critical skillset in todays world. And section 4 shows you how to apply all of the skills that youve already learned as you build database and GUI programs for the real world. |
| data science with c++: C++ Machine Learning Phil Culliton, 2017-12-29 Get introduced to the concepts of Machine Learning and build efficient data models in C++About This Book* Get introduced to the concepts of Machine Learning and see how you can implement them in C++, and build efficient data models for training data using popular libraries such as mlpack and Shark* A detailed guide packed with real-life examples to help you build a solid understanding of Machine Learning.Who This Book Is ForThe target audience is C++ developers who want to get into machine learning, or knowledgeable ML programmers who don't know C++ well but want to use it, and libraries written in it, in their work. The reader should be conversant with at least one programming language, and have some familiarity with strongly-typed languages and vectors/matrices.What you will learn* Model relationships in your data using supervised learning* Uncover insights using clustering and t-SNE* Use ensemble and stack to create more powerful models* Use cuda-convnet and deep learning to solve image recognition problems* Build an end-to-end pipeline that turns what you learn into practical, ready-to-use software* Solve big data problems using Hadoop and Google's MR4CIn DetailMachine Learning tasks are CPU time-consuming. C++ outperforms any other programming language by allowing access to programming constructs to optimize CPU-based number crunching, precision, and memory management normally abstracted away in higher-level languages.This book aims to address the challenges associated with C++ machine learning by introducing you to several useful libraries (mlpack, Shogun, and so on); you'll producing a library of your own code along the way that should make common tasks more straightforward.We begin with a review of the basic concepts you will need to know or brush up on before going further, including math and an intro to the C++ style we'll be using throughout the book. We then deal with the fundamentals of ML-how to handle input, the basic algorithms, and sample cases where the basic algorithms succeed or fail. This is followed by more advanced topics such as complex algorithms, regularization, optimization, and visualizing and understanding data, referring back to earlier work consistently so that you can see the mountains move. We'll then touch upon topics of current interest: computer vision (including sections on CUDA and deep learning), natural language processing, and handling very large datasets.The journey ends with a coda: we go back through the original sample cases, applying what we've learned along the way to rectify the issues we ran into initially. |
| data science with c++: Murach's C++ Programming Mary Delamater, Joel Murach, 2018-09 In the beginning, C++ was a hard language to learn because it required programmers to master low-level techniques to work with memory. Over the years, C++ has evolved to provide higher-level techniques that make it much easier to write effective code. But most C++ books havent evolved with the language. Until now. Now, this book uses modern C++ to get you off to a fast start, and then builds out your coding and OOP skills to the professional level. At that point, it also covers older techniques so youll be able to maintain the vast amount of legacy code thats out there, as well as work with embedded systems that dont support the newer techniques. |
| data science with c++: Data Structures and Algorithms in C++ Adam Drozdek, 2012-08-27 Strengthen your understanding of data structures and their algorithms for the foundation you need to successfully design, implement and maintain virtually any software system. Theoretical, yet practical, DATA STRUCUTRES AND ALGORITHMS IN C++, 4E by experienced author Adam Drosdek highlights the fundamental connection between data structures and their algorithms, giving equal weight to the practical implementation of data structures and the theoretical analysis of algorithms and their efficiency. This edition provides critical new coverage of treaps, k-d trees and k-d B-trees, generational garbage collection, and other advanced topics such as sorting methods and a new hashing technique. Abundant C++ code examples and a variety of case studies provide valuable insights into data structures implementation. DATA STRUCTURES AND ALGORITHMS IN C++ provides the balance of theory and practice to prepare readers for a variety of applications in a modern, object-oriented paradigm. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| data science with c++: C++ Classes and Data Structures Jeffrey S. Childs, 2008 Most books on data structures are filled with so many technical details (and lack thorough explanations) that the reading becomes difficult. This accessible, conversational presentation explores data structures concepts in clear language. Assumes a basic knowledge of C++. Focuses on the client for all programs, classes, and data structures. Offers meaningful, relevant examples and worked examples throughout. Includes thoroughly tested code. Provides code for all examples. A useful reference for anyone interested in learning more about programming. |
| data science with c++: Introduction to HPC with MPI for Data Science Frank Nielsen, 2016-02-03 This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions. Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters. In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics. Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. This part closes with a concise introduction to data core-sets that let big data problems be amenable to tiny data problems. Exercises are included at the end of each chapter in order for students to practice the concepts learned, and a final section contains an overall exam which allows them to evaluate how well they have assimilated the material covered in the book. |
| data science with c++: Data Structure Programming Joseph Bergin, 2012-12-06 This textbook provides an introduction to data structures and the Standard Template Library (STL), which has been recently accepted by the C++ Standards Committee. It provides a carefully integrated discussion of general data structures together with their implementation and use in the STL, thus teaching readers the important features of abstraction whilst using the STL to develop applications. |
| data science with c++: Introduction to C++ for Financial Engineers Daniel J. Duffy, 2013-10-24 This book introduces the reader to the C++ programming language and how to use it to write applications in quantitative finance (QF) and related areas. No previous knowledge of C or C++ is required -- experience with VBA, Matlab or other programming language is sufficient. The book adopts an incremental approach; starting from basic principles then moving on to advanced complex techniques and then to real-life applications in financial engineering. There are five major parts in the book: C++ fundamentals and object-oriented thinking in QF Advanced object-oriented features such as inheritance and polymorphism Template programming and the Standard Template Library (STL) An introduction to GOF design patterns and their applications in QF Applications The kinds of applications include binomial and trinomial methods, Monte Carlo simulation, advanced trees, partial differential equations and finite difference methods. This book includes a companion website with all source code and many useful C++ classes that you can use in your own applications. Examples, test cases and applications are directly relevant to QF. This book is the perfect companion to Daniel J. Duffy’s book Financial Instrument Pricing using C++ (Wiley 2004, 0470855096 / 9780470021620) |
| data science with c++: 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++. |
| data science with c++: Artificial Intelligence with Python Prateek Joshi, 2017-01-27 Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application. |
| data science with c++: Programming Bjarne Stroustrup, 2014 An introduction to programming by the inventor of C++, Programming prepares students for programming in the real world. This book assumes that they aim eventually to write non-trivial programs, whether for work in software development or in some other technical field. It explains fundamental concepts and techniques in greater depth than traditional introductions. This approach gives students a solid foundation for writing useful, correct, maintainable, and efficient code. This book is an introduction to programming in general, including object-oriented programming and generic programming. It is also a solid introduction to the C++ programming language, one of the most widely used languages for real-world software. It presents modern C++ programming techniques from the start, introducing the C++ standard library to simplify programming tasks. |
| data science with c++: Data-Driven Science and Engineering Steven L. Brunton, J. Nathan Kutz, 2022-05-05 A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®. |
| data science with c++: C++ Programming: From Problem Analysis to Program Design D. S. Malik, 2017-05-24 Learn how to program with C++ using today’s definitive choice for your first programming language experience -- C++ PROGRAMMING: FROM PROBLEM ANALYSIS TO PROGRAM DESIGN, 8E. D.S. Malik’s time-tested, user-centered methodology incorporates a strong focus on problem-solving with full-code examples that vividly demonstrate the hows and whys of applying programming concepts and utilizing C++ to work through a problem. Thoroughly updated end-of-chapter exercises, more than 20 extensive new programming exercises, and numerous new examples drawn from Dr. Malik’s experience further strengthen the reader’s understanding of problem solving and program design in this new edition. This book highlights the most important features of C++ 14 Standard with timely discussions that ensure this edition equips you to succeed in your first programming experience and well beyond. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version. |
| data science with c++: A Complete Guide to Programming in C++ Ulla Kirch-Prinz, Peter Prinz, 2002 This guide was written for readers interested in learning the C++ programming language from scratch, and for both novice and advanced C++ programmers wishing to enhance their knowledge of C++. The text is organized to guide the reader from elementary language concepts to professional software development, with in depth coverage of all the C++ language elements en route. |
| data science with c++: HT THINK LIKE A COMPUTER SCIEN Jeffrey Elkner, Allen B. Downey, Chris Meyers, 2016-10-04 The goal of this book is to teach you to think like a computer scientist. This way of thinking combines some of the best features of mathematics, engineering, and natural science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions. The single most important skill for a computer scientist is problem solving. Problem solving means the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problem-solving skills. That's why this chapter is called, The way of the program. On one level, you will be learning to program, a useful skill by itself. On another level, you will use programming as a means to an end. As we go along, that end will become clearer. |
| data science with c++: Data Science Programming All-in-One For Dummies John Paul Mueller, Luca Massaron, 2020-01-09 Your logical, linear guide to the fundamentals of data science programming Data science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models. Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionals What lies ahead: learn about specific areas that data is transforming Be meaningful: find out how to tell your data story See clearly: pick up the art of visualization Whether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s! |
| data science with c++: Introduction to C++ Programming and Graphics Constantine Pozrikidis, 2007-06-06 This book offers a venue for rapidly learning the language of C++ by concisely revealing its grammar, syntax and main features, and by explaining the key ideas behind object oriented programming (OOP) with emphasis on scientific computing. The book reviews elemental concepts of computers and computing, describes the primary features of C++, illustrates the use of pointers and user-defined functions, analyzes the construction of classes, and discusses graphics programming based on VOGLE and OpenGL. In short, the book is a basic, concise introduction to C++ programming for everyone from students to scientists and engineers seeking a quick grasp of key topics. |
| data science with c++: Algorithms, Data Structures, and Problem Solving with C++ Mark Allen Weiss, 1996 Providing a complete explanation of problem solving and algorithms using C++, the author's theoretical perspective emphasizes software engineering and object-oriented programming, and encourages readers to think abstractly. Numerous code examples and case studies are used to support the algorithms presented. |
| data science with c++: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open …
Belmont Forum Adopts Open Data Principles for Environme…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
