Durban's Lady in White: An Autobiography

MORLEY, Sam

Gibson, Perla Siedle

Biographies & Memoirs

VINE HOUSE DISTRIBUTION

By yon bonnie links: a 'mixed bag' of 14 auld clubs

Sports & Outdoors

Aedificamus Press

Back to Durban, 50 years on!: The "Lady in White" Memorial Visit of March '92 (with a marginal potted history of the city)

<p>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<br />
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.<br />
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.<br />
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<br />
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. Table of Contents An Introduction to Basic Packages, Functions, and Concepts Mathematical Plotting Using Matplotlib Calculus and differential equations Working with randomness Trees and networks Working with data and statistics Regression and forecasting Geometric problems Finding optimal solutions Improving your productivity</p>

Applying Math with Python - Second Edition: Over 70 practical recipes for solving real-world computational math problems

Computers & Technology

Computer Science

Packt Publishing

<p><p><b>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</b></p> <h4>Key Features</h4> <ul> <li>Compute complex mathematical problems using programming logic with the help of step-by-step recipes</li> <li>Learn how to utilize Python's libraries for computation, mathematical modeling, and statistics</li> <li>Discover simple yet effective techniques for solving mathematical equations and apply them in real-world statistics</li> </ul> <h4>Book Description</h4> <p>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.</p> <p>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.</p> <p>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.</p> <h4>What you will learn</h4> <ul> <li>Get familiar with basic packages, tools, and libraries in Python for solving mathematical problems</li> <li>Explore various techniques that will help you to solve computational mathematical problems</li> <li>Understand the core concepts of applied mathematics and how you can apply them in computer science</li> <li>Discover how to choose the most suitable package, tool, or technique to solve a certain problem</li> <li>Implement basic mathematical plotting, change plot styles, and add labels to the plots using Matplotlib</li> <li>Get to grips with probability theory with the Bayesian inference and Markov Chain Monte Carlo (MCMC) methods</li> </ul> <h4>Who this book is for</h4> <p>This book is for professional programmers and students looking to solve mathematical problems computationally using Python. Advanced mathematics knowledge is not a requirement, but a basic knowledge of mathematics will help you to get the most out of this book. The book assumes familiarity with Python concepts of data structures.</p></p>

Applying Math with Python: Practical recipes for solving computational math problems using Python programming and its libraries

Programming

<p>This debut collection from Sam Morley comprises poetry that is image-rich, probing and sublime. Committed to observation as a vehicle for discovery, Earshot is a book-length meditation deep in the mystery of the domestic sphere and the natural world. The poems are often profoundly personal while somehow removed, rippling out from Morley's unique inner perceptions into something universal and large. Pictures and diction progress with a distinctively strenuous, yet fluent movement - these are poems which deliver more in a few lines than many poems deliver in their entirety.</p>

Earshot

Literature & Fiction

Poetry

Puncher & Wattmann

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