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The Little Book Of Julia Algorithms
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Book Synopsis The Little Book of Julia Algorithms by : Ahan Sengupta
Download or read book The Little Book of Julia Algorithms written by Ahan Sengupta and published by . This book was released on 2021 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Little Book of Julia Algorithms by : Ahan Sengupta
Download or read book The Little Book of Julia Algorithms written by Ahan Sengupta and published by . This book was released on 2022-12 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Targeted at middle and high school programmers, this book aims to explain basic computer science concepts while teaching the Julia programming language. As a fast and productive high level language, Julia is ideal for beginner programmers. The learning curve for programming can be quite steep and this book aims to ease this transition by encouraging practise and gradually introducing more complex concepts. The book contains 50 programming challenges that encourages the reader to write their own programs. The solutions to all challenges are given at the end of the book. This book will make readers comfortable with using computers to solve any problems, and leave them well prepared for more significant programming in their maths, science or computer science courses at college. After finishing the exercises in this book, the reader should feel more familiar with: Loops and conditionals, Structuring code with functions, Reading and writing files, Installing and using packages, Sorting and searching, and Simple Statistics and Plotting. With a foreword by Jeff Bezanson, co-creator of the Julia programming language.
Download or read book Think Julia written by Ben Lauwens and published by "O'Reilly Media, Inc.". This book was released on 2019-04-05 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re just learning how to program, Julia is an excellent JIT-compiled, dynamically typed language with a clean syntax. This hands-on guide uses Julia 1.0 to walk you through programming one step at a time, beginning with basic programming concepts before moving on to more advanced capabilities, such as creating new types and multiple dispatch. Designed from the beginning for high performance, Julia is a general-purpose language ideal for not only numerical analysis and computational science but also web programming and scripting. Through exercises in each chapter, you’ll try out programming concepts as you learn them. Think Julia is perfect for students at the high school or college level as well as self-learners and professionals who need to learn programming basics. Start with the basics, including language syntax and semantics Get a clear definition of each programming concept Learn about values, variables, statements, functions, and data structures in a logical progression Discover how to work with files and databases Understand types, methods, and multiple dispatch Use debugging techniques to fix syntax, runtime, and semantic errors Explore interface design and data structures through case studies
Book Synopsis Algorithms from THE BOOK by : Kenneth Lange
Download or read book Algorithms from THE BOOK written by Kenneth Lange and published by SIAM. This book was released on 2020-05-04 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a dominant force in modern culture, and every indication is that they will become more pervasive, not less. The best algorithms are undergirded by beautiful mathematics. This text cuts across discipline boundaries to highlight some of the most famous and successful algorithms. Readers are exposed to the principles behind these examples and guided in assembling complex algorithms from simpler building blocks. Written in clear, instructive language within the constraints of mathematical rigor, Algorithms from THE BOOK includes a large number of classroom-tested exercises at the end of each chapter. The appendices cover background material often omitted from undergraduate courses. Most of the algorithm descriptions are accompanied by Julia code, an ideal language for scientific computing. This code is immediately available for experimentation. Algorithms from THE BOOK is aimed at first-year graduate and advanced undergraduate students. It will also serve as a convenient reference for professionals throughout the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences.
Book Synopsis The Little Book of Algorithms 2.0: A Workbook to Develop Fluency in Python Programming by : William Lau
Download or read book The Little Book of Algorithms 2.0: A Workbook to Develop Fluency in Python Programming written by William Lau and published by Little Book of Algorithms. This book was released on 2020-06-24 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This workbook is designed to help those learning and teaching Computer Science at secondary school level. The aim of the book is to help students build fluency in their Python programming. The book would suit students who have already been introduced to the three basic programming constructs of structured programming, namely sequence, selection and iteration. The learning curve for programming can be quite steep and this book aims to ease this transition by encouraging practise and gradually introducing more complex concepts such as lists and 2D lists and file writing. Originally, the book was written for 14-16 year old students studying for their GCSE Computer Science programming exam. However, a wide range of students and teachers will find this book useful.The Little Book of Algorithms concisely presents eighteen problems which computer science students will commonly encounter. These problems are solved efficiently using programs written using Python. However, reading these programs is not enough, so this new version of the book now comes with 48 challenges so that you can apply what you have learnt in various ways: Writing your own programsSolving Parson's puzzlesCompleting quizzes Tracing Gap fillsThis range of exercises will help you to become more fluent in Python and ensure that you are comfortable with any question format in a programming exam. Solutions are provided in the back and a series of video tutorials is also provided so that you can code along with the author, hearing his thought processes as he programs.After finishing this book, you should feel more familiar with: While loops and For loopsConcatenating different data typesUsing procedures and functionsWorking with 1D and 2D lists and arraysFile reading and writing This book will show you how to write better Python programs and will expose you to the key skills that are required to do well in any secondary school programming assignment or exa
Book Synopsis Numerical Linear Algebra with Julia by : Eric Darve
Download or read book Numerical Linear Algebra with Julia written by Eric Darve and published by SIAM. This book was released on 2021-09-02 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Linear Algebra with Julia provides in-depth coverage of fundamental topics in numerical linear algebra, including how to solve dense and sparse linear systems, compute QR factorizations, compute the eigendecomposition of a matrix, and solve linear systems using iterative methods such as conjugate gradient. Julia code is provided to illustrate concepts and allow readers to explore methods on their own. Written in a friendly and approachable style, the book contains detailed descriptions of algorithms along with illustrations and graphics that emphasize core concepts and demonstrate the algorithms. Numerical Linear Algebra with Julia is a textbook for advanced undergraduate and graduate students in most STEM fields and is appropriate for courses in numerical linear algebra. It may also serve as a reference for researchers in various fields who depend on numerical solvers in linear algebra.
Book Synopsis Algorithms for Optimization by : Mykel J. Kochenderfer
Download or read book Algorithms for Optimization written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2019-03-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.
Book Synopsis Julia Programming for Operations Research by : Changhyun Kwon
Download or read book Julia Programming for Operations Research written by Changhyun Kwon and published by Changhyun Kwon. This book was released on 2019-03-03 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Last Updated: December 2020 Based on Julia v1.3+ and JuMP v0.21+ The main motivation of writing this book was to help the author himself. He is a professor in the field of operations research, and his daily activities involve building models of mathematical optimization, developing algorithms for solving the problems, implementing those algorithms using computer programming languages, experimenting with data, etc. Three languages are involved: human language, mathematical language, and computer language. His team of students need to go over three different languages, which requires "translation" among the three languages. As this book was written to teach his research group how to translate, this book will also be useful for anyone who needs to learn how to translate in a similar situation. The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science. For more information, visit: http://www.chkwon.net/julia
Book Synopsis Algorithms for Decision Making by : Mykel J. Kochenderfer
Download or read book Algorithms for Decision Making written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2022-08-16 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Book Synopsis Numerical Algorithms by : Justin Solomon
Download or read book Numerical Algorithms written by Justin Solomon and published by CRC Press. This book was released on 2015-06-24 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig
Book Synopsis Dive Into Algorithms by : Bradford Tuckfield
Download or read book Dive Into Algorithms written by Bradford Tuckfield and published by No Starch Press. This book was released on 2021-01-05 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dive Into Algorithms is a broad introduction to algorithms using the Python Programming Language. Dive Into Algorithms is a wide-ranging, Pythonic tour of many of the world's most interesting algorithms. With little more than a bit of computer programming experience and basic high-school math, you'll explore standard computer science algorithms for searching, sorting, and optimization; human-based algorithms that help us determine how to catch a baseball or eat the right amount at a buffet; and advanced algorithms like ones used in machine learning and artificial intelligence. You'll even explore how ancient Egyptians and Russian peasants used algorithms to multiply numbers, how the ancient Greeks used them to find greatest common divisors, and how Japanese scholars in the age of samurai designed algorithms capable of generating magic squares. You'll explore algorithms that are useful in pure mathematics and learn how mathematical ideas can improve algorithms. You'll learn about an algorithm for generating continued fractions, one for quick calculations of square roots, and another for generating seemingly random sets of numbers. You'll also learn how to: • Use algorithms to debug code, maximize revenue, schedule tasks, and create decision trees • Measure the efficiency and speed of algorithms • Generate Voronoi diagrams for use in various geometric applications • Use algorithms to build a simple chatbot, win at board games, or solve sudoku puzzles • Write code for gradient ascent and descent algorithms that can find the maxima and minima of functions • Use simulated annealing to perform global optimization • Build a decision tree to predict happiness based on a person's characteristics Once you've finished this book you'll understand how to code and implement important algorithms as well as how to measure and optimize their performance, all while learning the nitty-gritty details of today's most powerful algorithms.
Book Synopsis Numerical Methods by : Anne Greenbaum
Download or read book Numerical Methods written by Anne Greenbaum and published by Princeton University Press. This book was released on 2012-04-01 with total page 471 pages. Available in PDF, EPUB and Kindle. Book excerpt: A rigorous and comprehensive introduction to numerical analysis Numerical Methods provides a clear and concise exploration of standard numerical analysis topics, as well as nontraditional ones, including mathematical modeling, Monte Carlo methods, Markov chains, and fractals. Filled with appealing examples that will motivate students, the textbook considers modern application areas, such as information retrieval and animation, and classical topics from physics and engineering. Exercises use MATLAB and promote understanding of computational results. The book gives instructors the flexibility to emphasize different aspects—design, analysis, or computer implementation—of numerical algorithms, depending on the background and interests of students. Designed for upper-division undergraduates in mathematics or computer science classes, the textbook assumes that students have prior knowledge of linear algebra and calculus, although these topics are reviewed in the text. Short discussions of the history of numerical methods are interspersed throughout the chapters. The book also includes polynomial interpolation at Chebyshev points, use of the MATLAB package Chebfun, and a section on the fast Fourier transform. Supplementary materials are available online. Clear and concise exposition of standard numerical analysis topics Explores nontraditional topics, such as mathematical modeling and Monte Carlo methods Covers modern applications, including information retrieval and animation, and classical applications from physics and engineering Promotes understanding of computational results through MATLAB exercises Provides flexibility so instructors can emphasize mathematical or applied/computational aspects of numerical methods or a combination Includes recent results on polynomial interpolation at Chebyshev points and use of the MATLAB package Chebfun Short discussions of the history of numerical methods interspersed throughout Supplementary materials available online
Book Synopsis Statistics with Julia by : Yoni Nazarathy
Download or read book Statistics with Julia written by Yoni Nazarathy and published by Springer Nature. This book was released on 2021-09-04 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence. The text does not require any prior statistical knowledge and only assumes a basic understanding of programming and mathematical notation. It is accessible to practitioners and researchers in data science, machine learning, bio-statistics, finance, or engineering who may wish to solidify their knowledge of probability and statistics. The book progresses through ten independent chapters starting with an introduction of Julia, and moving through basic probability, distributions, statistical inference, regression analysis, machine learning methods, and the use of Monte Carlo simulation for dynamic stochastic models. Ultimately this text introduces the Julia programming language as a computational tool, uniquely addressing end-users rather than developers. It makes heavy use of over 200 code examples to illustrate dozens of key statistical concepts. The Julia code, written in a simple format with parameters that can be easily modified, is also available for download from the book’s associated GitHub repository online. See what co-creators of the Julia language are saying about the book: Professor Alan Edelman, MIT: With “Statistics with Julia”, Yoni and Hayden have written an easy to read, well organized, modern introduction to statistics. The code may be looked at, and understood on the static pages of a book, or even better, when running live on a computer. Everything you need is here in one nicely written self-contained reference. Dr. Viral Shah, CEO of Julia Computing: Yoni and Hayden provide a modern way to learn statistics with the Julia programming language. This book has been perfected through iteration over several semesters in the classroom. It prepares the reader with two complementary skills - statistical reasoning with hands on experience and working with large datasets through training in Julia.
Book Synopsis The Constitution of Algorithms by : Florian Jaton
Download or read book The Constitution of Algorithms written by Florian Jaton and published by MIT Press. This book was released on 2021-04-27 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: A laboratory study that investigates how algorithms come into existence. Algorithms--often associated with the terms big data, machine learning, or artificial intelligence--underlie the technologies we use every day, and disputes over the consequences, actual or potential, of new algorithms arise regularly. In this book, Florian Jaton offers a new way to study computerized methods, providing an account of where algorithms come from and how they are constituted, investigating the practical activities by which algorithms are progressively assembled rather than what they may suggest or require once they are assembled.
Book Synopsis Hands-On Design Patterns and Best Practices with Julia by : Tom Kwong
Download or read book Hands-On Design Patterns and Best Practices with Julia written by Tom Kwong and published by Packt Publishing Ltd. This book was released on 2020-01-17 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design and develop high-performance, reusable, and maintainable applications using traditional and modern Julia patterns with this comprehensive guide Key FeaturesExplore useful design patterns along with object-oriented programming in Julia 1.0Implement macros and metaprogramming techniques to make your code faster, concise, and efficientDevelop the skills necessary to implement design patterns for creating robust and maintainable applicationsBook Description Design patterns are fundamental techniques for developing reusable and maintainable code. They provide a set of proven solutions that allow developers to solve problems in software development quickly. This book will demonstrate how to leverage design patterns with real-world applications. Starting with an overview of design patterns and best practices in application design, you'll learn about some of the most fundamental Julia features such as modules, data types, functions/interfaces, and metaprogramming. You'll then get to grips with the modern Julia design patterns for building large-scale applications with a focus on performance, reusability, robustness, and maintainability. The book also covers anti-patterns and how to avoid common mistakes and pitfalls in development. You'll see how traditional object-oriented patterns can be implemented differently and more effectively in Julia. Finally, you'll explore various use cases and examples, such as how expert Julia developers use design patterns in their open source packages. By the end of this Julia programming book, you'll have learned methods to improve software design, extensibility, and reusability, and be able to use design patterns efficiently to overcome common challenges in software development. What you will learnMaster the Julia language features that are key to developing large-scale software applicationsDiscover design patterns to improve overall application architecture and designDevelop reusable programs that are modular, extendable, performant, and easy to maintainWeigh up the pros and cons of using different design patterns for use casesExplore methods for transitioning from object-oriented programming to using equivalent or more advanced Julia techniquesWho this book is for This book is for beginner to intermediate-level Julia programmers who want to enhance their skills in designing and developing large-scale applications.
Book Synopsis Mastering Julia by : Malcolm Sherrington
Download or read book Mastering Julia written by Malcolm Sherrington and published by Packt Publishing Ltd. This book was released on 2015-07-22 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Julia is a well-constructed programming language with fast execution speed, eliminating the classic problem of performing analysis in one language and translating it for performance into a second. This book will help you develop and enhance your programming skills in Julia to solve real-world automation challenges. This book starts off with a refresher on installing and running Julia on different platforms. Next, you will compare the different ways of working with Julia and explore Julia's key features in-depth by looking at design and build. You will see how data works using simple statistics and analytics, and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks and observe how Julia can cooperate with external processes in order to enhance graphics and data visualization. Finally, you will look into meta-programming and learn how it adds great power to the language and establish networking and distributed computing with Julia.
Book Synopsis Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages by : Tanmay Bakshi
Download or read book Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages written by Tanmay Bakshi and published by McGraw Hill Professional. This book was released on 2019-12-06 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. A quick guide to start writing your own fun and useful Julia apps—no prior experience required! This engaging guide shows, step by step, how to build custom programs using Julia, the open-source, intuitive scripting language. Written by 15-year-old technology phenom Tanmay Bakshi, the book is presented in an accessible style that makes learning easy and enjoyable. Tanmay Teaches Julia for Beginners: A Springboard to Machine Learning for All Ages clearly explains the basics of Julia programming and takes a look at cutting-edge machine learning applications. You will also discover how to interface your Julia apps with code written in Python. Inside, you’ll learn to: • Set up and configure your Julia environment • Get up and running writing your own Julia apps • Define variables and use them in your programs • Use conditions, iterations, for-loops, and while-loops • Create, go through, and modify arrays • Build an app to manage things you lend and get back from your friends • Create and utilize dictionaries • Simplify maintenance of your code using functions • Apply functions on arrays and use functions recursively and generically • Understand and program basic machine learning apps