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دانلود کتاب Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems
دانلود کتاب کاربرد ریاضی با پایتون: بیش از 70 دستور العمل عملی برای حل مسائل ریاضی محاسباتی در دنیای واقعی
مشخصات کتاب
Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems
ویرایش: [2 ed.]
نویسندگان: Sam Morley
سری:
ISBN (شابک) : 1804618373, 9781804618370
ناشر: Packt Publishing
سال نشر: 2022
تعداد صفحات: 376
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 37 Mb
قیمت کتاب (تومان) : 30,000
در صورت تبدیل فایل کتاب Applying Math with Python: Over 70 practical recipes for solving real-world computational math problems به فرمت های PDF، EPUB، AZW3، MOBI و یا DJVU می توانید به پشتیبان اطلاع دهید تا فایل مورد نظر را تبدیل نمایند.
توجه داشته باشید کتاب کاربرد ریاضی با پایتون: بیش از 70 دستور العمل عملی برای حل مسائل ریاضی محاسباتی در دنیای واقعی نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب کاربرد ریاضی با پایتون: بیش از 70 دستور العمل عملی برای حل مسائل ریاضی محاسباتی در دنیای واقعی
راه‌حل‌ها و تکنیک‌های ساده را کشف کنید تا به شما در پیاده‌سازی مفاهیم ریاضی کاربردی مانند احتمال، حساب دیفرانسیل و انتگرال، و معادلات با استفاده از کتابخانه‌های عددی و علمی پایتون کمک کند. ویژگی های کلیدی • محاسبه مسائل پیچیده ریاضی با استفاده از منطق برنامه نویسی با کمک دستور العمل های گام به گام • یاد بگیرید چگونه از کتابخانه های پایتون برای محاسبات، مدل سازی ریاضی و آمار استفاده کنید • تکنیک های ساده و در عین حال موثر برای حل معادلات ریاضی را کشف کنید و آنها را در آمارهای دنیای واقعی به کار ببرید. توضیحات کتاب نسخه به روز شده Applying Math with Python به شما کمک می کند تا مسائل پیچیده را در طیف گسترده ای از زمینه های ریاضی به روش های ساده و کارآمد حل کنید. دستور العمل های قدیمی برای کتابخانه های جدید تجدید نظر شده اند و چندین دستور غذا برای نشان دادن ابزارهای جدید مانند JAX اضافه شده اند. شما با تازه کردن دانش خود از چندین زمینه اصلی ریاضی شروع خواهید کرد و در مورد بسته های پوشش داده شده در پشته علمی پایتون، از جمله NumPy، SciPy، و Matplotlib آشنا خواهید شد. همانطور که پیشرفت می کنید، به تدریج با موضوعات پیشرفته تر حساب، احتمالات و شبکه ها (نظریه گراف) آشنا خواهید شد. هنگامی که پایه محکمی در این موضوعات ایجاد کردید، این اعتماد به نفس خواهید داشت که در حین بررسی برنامه های کاربردی پایتون در علم داده و آمار، پیش بینی، هندسه و بهینه سازی، به ماجراجویی های ریاضی با پایتون بپردازید. فصل های آخر شما را از طریق مجموعه ای از مشکلات متفرقه، از جمله کار با فرمت های داده خاص و کدهای شتاب دهنده راهنمایی می کند. در پایان این کتاب، زرادخانه ای از راه حل های کدگذاری عملی خواهید داشت که می توانند برای حل طیف گسترده ای از مسائل عملی در ریاضیات محاسباتی و علوم داده مورد استفاده و اصلاح قرار گیرند. آنچه خواهید آموخت • با بسته ها، ابزارها و کتابخانه های پایه پایتون برای حل مسائل ریاضی آشنا شوید. • کاربردهای واقعی ریاضیات را برای کاهش مشکل در بهینه سازی کاوش کنید • مفاهیم اصلی ریاضیات کاربردی و کاربرد آنها در علوم کامپیوتر را درک کنید • نحوه انتخاب مناسب ترین بسته، ابزار یا تکنیک برای حل یک مشکل را بیابید • با استفاده از Matplotlib رسم های ریاضی را پیاده سازی کنید، سبک های نمودار را تغییر دهید، و برچسب ها را به نمودارها اضافه کنید. • با روش های استنتاج بیزی و زنجیره مارکوف مونت کارلو (MCMC) با نظریه احتمال آشنا شوید. این کتاب برای چه کسی است چه یک برنامه نویس حرفه ای باشید و چه دانشجویی که به دنبال حل مسائل ریاضی به صورت محاسباتی با استفاده از پایتون هستید، این کتاب برای شما مناسب است. مهارت ریاضیات پیشرفته پیش نیاز نیست، اما دانش پایه ریاضی به شما کمک می کند تا از این کتاب ریاضی پایتون بیشترین بهره را ببرید. آشنایی با مفاهیم ساختار داده در پایتون فرض می شود.
توضیحاتی درمورد کتاب به خارجی
Discover easy-to-follow solutions and techniques to help you to implement applied mathematical concepts such as probability, calculus, and equations using Python's numeric and scientific libraries Key Features • Compute complex mathematical problems using programming logic with the help of step-by-step recipes • Learn how to use Python libraries for computation, mathematical modeling, and statistics • Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics Book Description The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you've developed a solid base in these topics, you'll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science. What you will learn • Become familiar with basic Python packages, tools, and libraries for solving mathematical problems • Explore real-world applications of mathematics to reduce a problem in optimization • Understand the core concepts of applied mathematics and their application in computer science • Find out how to choose the most suitable package, tool, or technique to solve a problem • Implement basic mathematical plotting, change plot styles, and add labels to plots using Matplotlib • Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods Who this book is for Whether you are a professional programmer or a student looking to solve mathematical problems computationally using Python, this is the book for you. Advanced mathematics proficiency is not a prerequisite, but basic knowledge of mathematics will help you to get the most out of this Python math book. Familiarity with the concepts of data structures in Python is assumed.
فهرست مطالب
Cover Title Page Copyright Dedication Contributors Table of Contents Preface Chapter 1: An Introduction to Basic Packages, Functions, and Concepts Technical requirements Exploring Python numerical types Decimal type Fraction type Complex type Understanding basic mathematical functions Diving into the world of NumPy Element access Array arithmetic and functions Useful array creation routines Higher-dimensional arrays Working with matrices and linear algebra Basic methods and properties Matrix multiplication Determinants and inverses Systems of equations Eigenvalues and eigenvectors Sparse matrices Summary Further reading Chapter 2: Mathematical Plotting with Matplotlib Technical requirements Basic plotting with Matplotlib Getting ready How to do it... How it works… There’s more… Adding subplots Getting ready How to do it... How it works... There’s more... See also Plotting with error bars Getting ready How to do it… How it works… There’s more... Saving Matplotlib figures Getting ready How to do it... How it works... There’s more... See also Surface and contour plots Getting ready How to do it... How it works... There’s more... See also Customizing three-dimensional plots Getting ready How to do it... How it works... There’s more... Plotting vector fields with quiver plots Getting ready How to do it… How it works… There’s more… Further reading Chapter 3: Calculus and Differential Equations Technical requirements Primer on calculus Working with polynomials and calculus Getting ready How to do it... How it works... There’s more... See also Differentiating and integrating symbolically using SymPy Getting ready How to do it... How it works... There’s more... Solving equations Getting ready How to do it... How it works... There’s more... Integrating functions numerically using SciPy Getting ready How to do it... How it works... There’s more... Solving simple differential equations numerically Getting ready How to do it... How it works... There’s more... See also Solving systems of differential equations Getting ready How to do it... How it works... There’s more... Solving partial differential equations numerically Getting ready How to do it... How it works... There’s more... See also Using discrete Fourier transforms for signal processing Getting ready How to do it... How it works... There’s more... See also Automatic differentiation and calculus using JAX Getting ready How to do it… How it works… There’s more… See also Solving differential equations using JAX Getting ready How to do it… How it works… See also Further reading Chapter 4: Working with Randomness and Probability Technical requirements Selecting items at random Getting ready How to do it... How it works... There’s more... Generating random data Getting ready How to do it... How it works... There’s more... Changing the random number generator Getting ready How to do it... How it works... There’s more... Generating normally distributed random numbers Getting ready How to do it... How it works... There’s more... Working with random processes Getting ready How to do it... How it works... There’s more... Analyzing conversion rates with Bayesian techniques Getting ready How to do it... How it works... There’s more... Estimating parameters with Monte Carlo simulations Getting ready How to do it... How it works... There’s more... See also Further reading Chapter 5: Working with Trees and Networks Technical requirements Creating networks in Python Getting ready How to do it... How it works... There’s more... Visualizing networks Getting ready How to do it... How it works... There’s more... Getting the basic characteristics of networks Getting ready How to do it... How it works... There’s more... Generating the adjacency matrix for a network Getting ready How to do it... How it works... There’s more... Creating directed and weighted networks Getting ready How to do it... How it works... There’s more... Finding the shortest paths in a network Getting ready How to do it... How it works... There’s more... Quantifying clustering in a network Getting ready How to do it... How it works... There’s more... Coloring a network Getting ready How to do it... How it works... There’s more... Finding minimal spanning trees and dominating sets Getting ready How to do it... How it works... Further reading Chapter 6: Working with Data and Statistics What is statistics? Technical requirements Creating Series and DataFrame objects Getting ready How to do it... How it works... There’s more... See also Loading and storing data from a DataFrame Getting ready How to do it... How it works... See also Manipulating data in DataFrames Getting ready How to do it... How it works... There’s more... Plotting data from a DataFrame Getting ready How to do it... How it works... There’s more... Getting descriptive statistics from a DataFrame Getting ready How to do it... How it works... There’s more... Understanding a population using sampling Getting ready How to do it... How it works... See also Performing operations on grouped data in a DataFrame Getting ready How to do it... How it works... Testing hypotheses using t-tests Getting ready How to do it... How it works... There’s more... Testing hypotheses using ANOVA Getting ready How to do it... How it works... There’s more... Testing hypotheses for non-parametric data Getting ready How to do it... How it works... Creating interactive plots with Bokeh Getting ready How to do it... How it works... There’s more... Further reading Chapter 7: Using Regression and Forecasting Technical requirements Getting ready How to do it... How it works... There’s more... Using multilinear regression Getting ready How to do it... How it works... Classifying using logarithmic regression Getting ready How to do it... How it works... There’s more... Modeling time series data with ARMA Getting ready How to do it... How it works... There’s more... Forecasting from time series data using ARIMA Getting ready How to do it... How it works... Forecasting seasonal data using ARIMA Getting ready How to do it... How it works... There’s more... Using Prophet to model time series data Getting ready How to do it... How it works... There’s more... Using signatures to summarize time series data Getting ready How to do it… How it works… There’s more… See also Further reading Chapter 8: Geometric Problems Technical requirements Visualizing two-dimensional geometric shapes Getting ready How to do it... How it works... There’s more... See also Finding interior points Getting ready How to do it... How it works... Finding edges in an image Getting ready How to do it… How it works... Triangulating planar figures Getting ready How to do it... How it works... There’s more... See also Computing convex hulls Getting ready How to do it... How it works... Constructing Bezier curves Getting ready How to do it... How it works... There’s more... Further reading Chapter 9: Finding Optimal Solutions Technical requirements Minimizing a simple linear function Getting ready How to do it... How it works... There’s more... Minimizing a non-linear function Getting ready How to do it... How it works... There’s more... Using gradient descent methods in optimization Getting ready How to do it... How it works... There’s more... Using least squares to fit a curve to data Getting ready How to do it... How it works... There’s more... Analyzing simple two-player games Getting ready How to do it... How it works... There’s more... Computing Nash equilibria Getting ready How to do it... How it works... There’s more... See also Further reading Chapter 10: Improving Your Productivity Technical requirements Keeping track of units with Pint Getting ready How to do it... How it works... There’s more... Accounting for uncertainty in calculations Getting ready How to do it... How it works... There’s more... Loading and storing data from NetCDF files Getting ready How to do it... How it works... There’s more... Working with geographical data Getting ready How to do it... How it works... Executing a Jupyter notebook as a script Getting ready How to do it... How it works... There’s more... Validating data Getting ready How to do it... How it works... Accelerating code with Cython Getting ready How to do it... How it works... There’s more... Distributing computing with Dask Getting ready How to do it... How it works... There’s more... Writing reproducible code for data science Getting ready How to do it… How it works… There’s more… See also... Index About Packt Other Books You May Enjoy
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