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دانلود کتاب Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries
دانلود کتاب کاربرد ریاضی با پایتون: دستور العمل های عملی برای حل مسائل ریاضی محاسباتی با استفاده از برنامه نویسی پایتون و کتابخانه های آن
مشخصات کتاب
Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries
ویرایش:
نویسندگان: Sam Morley
سری:
ISBN (شابک) : 1838989757, 9781838989750
ناشر: Packt Publishing
سال نشر: 2020
تعداد صفحات: 353
زبان: English
فرمت فایل : PDF (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود)
حجم فایل: 18 مگابایت
قیمت کتاب (تومان) : 31,000
در صورت تبدیل فایل کتاب Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries 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: دستور العمل های عملی برای حل مسائل ریاضی محاسباتی با استفاده از برنامه نویسی پایتون و کتابخانه های آن 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: دستور العمل های عملی برای حل مسائل ریاضی محاسباتی با استفاده از برنامه نویسی پایتون و کتابخانه های آن
پایتون، یکی از محبوب ترین زبان های برنامه نویسی جهان، دارای تعدادی بسته قدرتمند است که به شما کمک می کند تا مسائل پیچیده ریاضی را به روشی ساده و کارآمد حل کنید. این قابلیت های اصلی به برنامه نویسان کمک می کند تا با استفاده از دانش در حوزه ریاضیات محاسباتی، راه را برای ساخت برنامه های کاربردی هیجان انگیز در حوزه های مختلف، مانند یادگیری ماشین و علم داده، هموار کنند. این کتاب به شما می آموزد که چگونه مسائلی را که در زمینه های مختلف ریاضی از جمله حساب دیفرانسیل و انتگرال، احتمالات، آمار و علوم داده، تئوری گراف، بهینه سازی و هندسه با آن مواجه می شوند، حل کنید. شما با توسعه مهارت‌های اصلی و یادگیری در مورد بسته‌هایی که در پشته علمی پایتون شامل NumPy، SciPy و Matplotlib هستند، شروع می‌کنید. با پیشروی، با موضوعات پیشرفته تری مانند حساب دیفرانسیل و انتگرال، احتمالات و شبکه ها (نظریه گراف) آشنا خواهید شد. پس از به دست آوردن درک کامل از این موضوعات، برنامه های کاربردی پایتون را در علم داده و آمار، پیش بینی، هندسه و بهینه سازی کشف خواهید کرد. 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.
توضیحاتی درمورد کتاب به خارجی
Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover 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.
فهرست مطالب
Cover Title Page Copyright and Credits Dedication About Packt Contributors Table of Contents Preface Chapter 1: Basic Packages, Functions, and Concepts Technical requirements Python numerical types Decimal type Fraction type Complex type Basic mathematical functions NumPy arrays Element access Array arithmetic and functions Useful array creation routines Higher dimensional arrays Matrices 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... Changing the plotting style Getting ready How to do it... How it works... There's more... Adding labels and legends to plots How to do it... How it works... Adding subplots Getting ready How to do it... How it works... There's more... See also 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... Customizing three-dimensional plots Getting ready How to do it... How it works... There's more... Further reading Chapter 3: Calculus and Differential Equations Technical requirements 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 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 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 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: Regression and Forecasting Technical requirements Using basic linear regression 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... 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: Miscellaneous Topics 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... Working with data streams Getting ready How to do it... How it works... See also 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... Other Books You May Enjoy Index
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