Search results for "python arithmetic"
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Artificial Intelligence Programming with Python
A hands-on roadmap to using Python for artificial intelligence programming In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simple and plain language and with illustrative code examples. Divided into three parts, the author explains artificial intelligence generally, machine learning, and deep learning. It tackles a wide variety of useful topics, from classification and regression in machine learning to generative adversarial networks. He also includes: Fulsome introductions to MATLAB, Python, AI, machine learning, and deep learningExpansive discussions on supervised and unsupervised machine learning, as well as semi-supervised learningPractical AI and Python “cheat sheet” quick referencesThis hands-on AI programming guide is perfect for anyone with a basic knowledge of programming—including familiarity with variables, arrays, loops, if-else statements, and file input and output—who seeks to understand foundational concepts in AI and AI development.
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Practical Data Science with Python
Learn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
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Numerische Methoden bei Integralen und gewöhnlichen Differentialgleichungen für programmierbare Taschenrechner (AOS)
In diesem Buch werden einige Verfahren zur numerischen Losung von bestimmten Integralen und Anfangs-, Rand-und Eigenwertaufgaben bei gewohnlichen Differentialgleichungen beschrieben und in Programme fUr Taschenrechner der Serie TI-58 oder TI-59 umgesetzt. Das Buch kann damit demjenigen, der lediglich ein konkretes Problem aus dem hier behandelten Themenkreis losen mochte, als Programmsammlung dienen. In diesem Fall braucht nur aus dem Katalog der 33 effi- zienten Programme (s. Anhang) das geeignete herausgesucht und unter Beachtung der zuge- horigen Benutzeranleitung gestartet zu werden. Vornehmlich aber wendet sich dieses Buch an den Leser, der das umfangreiche Gebiet der numeri- schen Methoden bei Differentialgleichungen kennenlernen und sich bei einem ersten Studium nicht durch eine allzu theoretische Darstellung abschrecken lassen mochte. Die meisten Mathe- matiker begeistern sich naturgemar! hauptsiichlich fiir eine moglichst umfassende Theorie mit schonen und mathematisch strengen Beweisen. Es fallt ihnen nicht immer leicht einzusehen, dar! viele Menschen diese Begeisterung nicht in gleichem Mar!e teilen und stattdessen lieber ein prak- tisches oder zumindest ein numerisches Beispiel vorziehen. Aus diesem Grund wird in diesem Buch auf die Theorie der Verfahren kaum eingegangen. Diese findet der Leser in manchen der zahl- reichen Lehrbiicher iiber numerische Mathematik, von denen einige im Literaturverzeichnis ange- geben sind. Wir werden also auf den folgenden Seiten die jeweiligen Verfahren an vielen Beispielen erproben.
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Python for ArcGIS Pro
Extend your ArcGIS expertise by unlocking the world of Python programming. A fully hands-on guide that takes you through exercise after exercise using real data and real problems. NOTE: This book is compatible with ArcGIS Pro 2.9.Key FeaturesLearn the core components of the two Python modules for ArcGIS: ArcPy and ArcGIS API for PythonUse ArcPy, pandas, NumPy, and ArcGIS in ArcGIS Pro Notebooks to manage and analyze geospatial data at scaleIntegrate with ArcGIS Online using Python to publish and manage dataBook DescriptionIntegrating Python into your day-to-day ArcGIS work is highly recommended when dealing with large amounts of geospatial data. Python for ArcGIS Pro aims to help you get your work done faster, with greater repeatability and higher confidence in your results. Starting from programming basics and building in complexity, two experienced ArcGIS professionals-turned-Python programmers teach you how to incorporate scripting at each step: automating the production of maps for print, managing data between ArcGIS Pro and ArcGIS Online, creating custom script tools for sharing, and then running data analysis and visualization on top of the ArcGIS geospatial library, all using Python. You'll use ArcGIS Pro Notebooks to explore and analyze geospatial data, and write data engineering scripts to manage ongoing data processing and data transfers. This exercise-based book also includes three rich real-world case studies, giving you an opportunity to apply and extend the concepts you studied earlier. Irrespective of your expertise level with Esri software or the Python language, you'll benefit from this book's hands-on approach, which takes you through the major uses of Python for ArcGIS Pro to boost your ArcGIS productivity.What you will learnAutomate map production to make and edit maps at scale, cutting down on repetitive tasksPublish map layer data to ArcGIS OnlineAutomate data updates using the ArcPy Data Access module and cursorsTurn your scripts into script tools for ArcGIS ProLearn how to manage data on ArcGIS OnlineQuery, edit, and append to feature layers and create symbology with renderers and colorizersApply pandas and NumPy to raster and vector analysisLearn new tricks to manage data for entire cities or large companiesWho this book is forThis book is ideal for anyone looking to add Python to their ArcGIS Pro workflows, even if you have no prior experience with programming. This includes ArcGIS professionals, intermediate ArcGIS Pro users, ArcGIS Pro power users, students, and people who want to move from being a GIS Technician to GIS Analyst; GIS Analyst to GIS Programmer; or GIS Developer/Programmer to a GIS Architect. Basic familiarity with geospatial/GIS syntax, ArcGIS, and data science (pandas) is helpful, though not necessary.
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Computer Arithmetic
Ideal for graduate and senior undergraduate courses in computer arithmetic and advanced digital design, Computer Arithmetic: Algorithms and Hardware Designs, Second Edition, provides a balanced, comprehensive treatment of computer arithmetic. It covers topics in arithmetic unit design and circuit implementation that complement the architectural and algorithmic speedup techniques used in high-performance computer architecture and parallel processing. Using a unified and consistent framework, the text begins with number representation and proceeds through basic arithmetic operations, floating-point arithmetic, and function evaluation methods. Later chapters cover broad design and implementation topics-including techniques for high-throughput, low-power, fault-tolerant, and reconfigurable arithmetic. An appendix provides a historical view of the field and speculates on its future.An indispensable resource for instruction, professional development, and research, Computer Arithmetic: Algorithms and Hardware Designs, Second Edition, combines broad coverage of the underlying theories of computer arithmetic with numerous examples of practical designs, worked-out examples, and a large collection of meaningful problems. This second edition includes a new chapter on reconfigurable arithmetic, in order to address the fact that arithmetic functions are increasingly being implemented on field-programmable gate arrays (FPGAs) and FPGA-like configurable devices. Updated and thoroughly revised, the book offers new and expanded coverage of saturating adders and multipliers, truncated multipliers, fused multiply-add units, overlapped quotient digit selection, bipartite and multipartite tables, reversible logic, dot notation, modular arithmetic, Montgomery modular reduction, division by constants, IEEE floating-point standard formats, and interval arithmetic.Features: * Divided into 28 lecture-size chapters * Emphasizes both the underlying theories of computer arithmetic and actual hardware designs * Carefully links computer arithmetic to other subfields of computer engineering * Includes 717 end-of-chapter problems ranging in complexity from simple exercises to mini-projects * Incorporates many examples of practical designs * Uses consistent standardized notation throughout * Instructor's manual includes solutions to text problems * An author-maintained website http://www.ece.ucsb.edu/~parhami/text comp arit.htm contains instructor resources, including complete lecture slides
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Essential PySpark for Scalable Data Analytics
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scaleKey FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook DescriptionApache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems.What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is forThis book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
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Python Data Science Essentials
Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.
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