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| elegant scipy: Elegant SciPy Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 2017-08-11 Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library |
| elegant scipy: Elegant SciPy Juan Nunez-Iglesias, Stéfan van der Walt, Harriet Dashnow, 2017-08-11 Welcome to Scientific Python and its community. If you’re a scientist who programs with Python, this practical guide not only teaches you the fundamental parts of SciPy and libraries related to it, but also gives you a taste for beautiful, easy-to-read code that you can use in practice. You’ll learn how to write elegant code that’s clear, concise, and efficient at executing the task at hand. Throughout the book, you’ll work with examples from the wider scientific Python ecosystem, using code that illustrates principles outlined in the book. Using actual scientific data, you’ll work on real-world problems with SciPy, NumPy, Pandas, scikit-image, and other Python libraries. Explore the NumPy array, the data structure that underlies numerical scientific computation Use quantile normalization to ensure that measurements fit a specific distribution Represent separate regions in an image with a Region Adjacency Graph Convert temporal or spatial data into frequency domain data with the Fast Fourier Transform Solve sparse matrix problems, including image segmentations, with SciPy’s sparse module Perform linear algebra by using SciPy packages Explore image alignment (registration) with SciPy’s optimize module Process large datasets with Python data streaming primitives and the Toolz library |
| elegant scipy: Optimization for Machine Learning Jason Brownlee, 2021-09-22 Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms. |
| elegant scipy: Basics of Linear Algebra for Machine Learning Jason Brownlee, 2018-01-24 Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more. |
| elegant scipy: Clean Python Sunil Kapil, 2019-05-21 Discover the right way to code in Python. This book provides the tips and techniques you need to produce cleaner, error-free, and eloquent Python projects. Your journey to better code starts with understanding the importance of formatting and documenting your code for maximum readability, utilizing built-in data structures and Python dictionary for improved maintainability, and working with modules and meta-classes to effectively organize your code. You will then dive deep into the new features of the Python language and learn how to effectively utilize them. Next, you will decode key concepts such as asynchronous programming, Python data types, type hinting, and path handling. Learn tips to debug and conduct unit and integration tests in your Python code to ensure your code is ready for production. The final leg of your learning journey equips you with essential tools for version management, managing live code, and intelligent code completion. After reading and using this book, you will be proficient in writing clean Python code and successfully apply these principles to your own Python projects. What You’ll Learn Use the right expressions and statements in your Python code Create and assess Python Dictionary Work with advanced data structures in Python Write better modules, classes, functions, and metaclasses Start writing asynchronous Python immediately Discover new features in Python Who This Book Is For Readers with a basic Python programming knowledge who want to improve their Python programming skills by learning right way to code in Python. |
| elegant scipy: Data Science for Neuroimaging Ariel Rokem, Tal Yarkoni, 2023-12-12 Data science methods and tools—including programming, data management, visualization, and machine learning—and their application to neuroimaging research As neuroimaging turns toward data-intensive discovery, researchers in the field must learn to access, manage, and analyze datasets at unprecedented scales. Concerns about reproducibility and increased rigor in reporting of scientific results also demand higher standards of computational practice. This book offers neuroimaging researchers an introduction to data science, presenting methods, tools, and approaches that facilitate automated, reproducible, and scalable analysis and understanding of data. Through guided, hands-on explorations of openly available neuroimaging datasets, the book explains such elements of data science as programming, data management, visualization, and machine learning, and describes their application to neuroimaging. Readers will come away with broadly relevant data science skills that they can easily translate to their own questions. • Fills the need for an authoritative resource on data science for neuroimaging researchers • Strong emphasis on programming • Provides extensive code examples written in the Python programming language • Draws on openly available neuroimaging datasets for examples • Written entirely in the Jupyter notebook format, so the code examples can be executed, modified, and re-executed as part of the learning process |
| elegant scipy: Python for Finance Dmytro Zherlitsyn, 2024-07-30 DESCRIPTION Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis. This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. KEY FEATURES ● Comprehensive guide to Python for financial data analysis and modeling. ● Practical examples and real-world applications for immediate implementation. ● Covers advanced topics like regression, Machine Learning and time series forecasting. WHAT YOU WILL LEARN ● Learn financial data analysis using Python data science libraries and techniques. ● Learn Python visualization tools to justify investment and trading strategies. ● Learn asset pricing and portfolio management methods with Python. ● Learn advanced regression and time series models for financial forecasting. ● Learn risk assessment and volatility modeling methods with Python. WHO THIS BOOK IS FOR This book is designed for financial analysts and other professionals interested in the financial industry with a basic understanding of Python programming and statistical analysis. It is also suitable for students in finance and data science who wish to apply Python tools to financial data analysis and decision-making. TABLE OF CONTENTS 1. Getting Started with Python for Finance 2. Python Tools for Data Analysis: Primer to Pandas and NumPy 3. Financial Data Manipulation with Python 4. Exploratory Data Analysis for Finance 5. Investment and Trading Strategies 6. Asset Pricing and Portfolio Management 7. Time Series Analysis and Financial Data Forecasting 8. Risk Assessment and Volatility Modelling 9. Machine Learning and Deep Learning in Finance 10. Time Series Analysis and Forecasting with FB Prophet Library Appendix A: Python Code Examples for Finance Appendix B: Glossary Appendix C: Valuable Resources |
| elegant scipy: Financial Data Analysis Using Python Dmytro Zherlitsyn, 2025-05-20 This book will introduce essential concepts in financial analysis methods & models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will also help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using the Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. FEATURES • Illustrates financial data analysis using Python data science libraries & techniques • Uses Python visualization tools to justify investment and trading strategies • Covers asset pricing & portfolio management methods with Python |
| elegant scipy: Numerical Python Robert Johansson, 2024-09-27 Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more. Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning. What You'll Learn Work with vectors and matrices using NumPy Review Symbolic computing with SymPy Plot and visualize data with Matplotlib Perform data analysis tasks with Pandas and SciPy Understand statistical modeling and machine learning with statsmodels and scikit-learn Optimize Python code using Numba and Cython Who This Book Is For Developers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis. |
| elegant scipy: Элегантный SciPy Хуан Нуньес-Иглесиас, Штефан ван дер Уолт, Харриет Дэшноу, 2022-01-29 Книга познакомит вас с основополагающими компонентами библиотеки SciPy языка Python. Вы научитесь писать элегантный, ясный, краткий и эффективный программный код благодаря примерам из обширной научной экосистемы Python. Кроме SciPy, вы узнаете много нового про сопутствующие библиотеки, такие как NumPy, Pandas, scikit-image.Издание будет полезно всем программистам на Python, желающим использовать научные библиотеки в своей работе. |
| elegant scipy: Python for Data Analysis Wes McKinney, 2017-09-25 Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples |
| elegant scipy: Python for Scientists John M. Stewart, 2017-07-20 Scientific Python is taught from scratch in this book via copious, downloadable, useful and adaptable code snippets. Everything the working scientist needs to know is covered, quickly providing researchers and research students with the skills to start using Python effectively. |
| elegant scipy: Python for Finance Yves Hilpisch, 2014-12-11 The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies |
| elegant scipy: Python for Finance Yves J. Hilpisch, 2018-12-05 The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks. |
| elegant scipy: Artificial Intelligence with Python Teik Toe Teoh, Zheng Rong, 2022-03-16 Entering the field of artificial intelligence and data science can seem daunting to beginners with little to no prior background, especially those with no programming experience. The concepts used in self-driving cars and virtual assistants like Amazon’s Alexa may seem very complex and difficult to grasp. The aim of Artificial Intelligence in Python is to make AI accessible and easy to understand for people with little to no programming experience though practical exercises. Newcomers will gain the necessary knowledge on how to create such systems, which are capable of executing tasks that require some form of human-like intelligence. This book introduces readers to various topics and examples of programming in Python, as well as key concepts in artificial intelligence. Python programming skills will be imparted as we go along. Concepts and code snippets will be covered in a step-by-step manner, to guide and instill confidence in beginners. Complex subjects in deep learning and machine learning will be broken down into easy-to-digest content and examples. Artificial intelligence implementations will also be shared, allowing beginners to generate their own artificial intelligence algorithms for reinforcement learning, style transfer, chatbots, speech, and natural language processing. |
| elegant scipy: Python - Apprentissage actif - Pour l'étudiant et le futur enseignant Jean Roy, 2020-03-03 Le langage Python permet dans l'industrie de piloter des systèmes complexes, mais il se répand aussi dans le monde de l'éducation, du lycée à l'université, par sa simplicité et ses bibliothèques logicielles, mathématiques notamment. Ce livre présente l'essentiel du langage Python puis, à travers 130 exercices et problèmes, propose un entraînement au codage. Partant de la base algorithmique classique, il aborde ensuite des secteurs moins enseignés comme le graphisme, l'analyse lexicale et syntaxique, les moteurs physiques, l'intelligence artificielle (génétique, neurones), et la musique électronique. |
| elegant scipy: Springer Handbook of Atomic, Molecular, and Optical Physics Gordon W. F. Drake, 2023-02-09 Comprises a comprehensive reference source that unifies the entire fields of atomic molecular and optical (AMO) physics, assembling the principal ideas, techniques and results of the field. 92 chapters written by about 120 authors present the principal ideas, techniques and results of the field, together with a guide to the primary research literature (carefully edited to ensure a uniform coverage and style, with extensive cross-references). Along with a summary of key ideas, techniques, and results, many chapters offer diagrams of apparatus, graphs, and tables of data. From atomic spectroscopy to applications in comets, one finds contributions from over 100 authors, all leaders in their respective disciplines. Substantially updated and expanded since the original 1996 edition, it now contains several entirely new chapters covering current areas of great research interest that barely existed in 1996, such as Bose-Einstein condensation, quantum information, and cosmological variations of the fundamental constants. A fully-searchable CD- ROM version of the contents accompanies the handbook. |
| elegant scipy: A Guide to NumPy Travis E. Oliphant, 2006 |
| elegant scipy: Programming for Computations - Python Svein Linge, Hans Petter Langtangen, 2016-07-25 This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification. |
| elegant scipy: Image Processing and Acquisition using Python Ravishankar Chityala, Sridevi Pudipeddi, 2020-06-11 Image Processing and Acquisition using Python provides readers with a sound foundation in both image acquisition and image processing—one of the first books to integrate these topics together. By improving readers’ knowledge of image acquisition techniques and corresponding image processing, the book will help them perform experiments more effectively and cost efficiently as well as analyze and measure more accurately. Long recognized as one of the easiest languages for non-programmers to learn, Python is used in a variety of practical examples. A refresher for more experienced readers, the first part of the book presents an introduction to Python, Python modules, reading and writing images using Python, and an introduction to images. The second part discusses the basics of image processing, including pre/post processing using filters, segmentation, morphological operations, and measurements. The second part describes image acquisition using various modalities, such as x-ray, CT, MRI, light microscopy, and electron microscopy. These modalities encompass most of the common image acquisition methods currently used by researchers in academia and industry. Features Covers both the physical methods of obtaining images and the analytical processing methods required to understand the science behind the images. Contains many examples, detailed derivations, and working Python examples of the techniques. Offers practical tips on image acquisition and processing. Includes numerous exercises to test the reader’s skills in Python programming and image processing, with solutions to selected problems, example programs, and images available on the book’s web page. New to this edition Machine learning has become an indispensable part of image processing and computer vision, so in this new edition two new chapters are included: one on neural networks and the other on convolutional neural networks. A new chapter on affine transform and many new algorithms. Updated Python code aligned to the latest version of modules. |
| elegant scipy: Introducing Python Bill Lubanovic, 2019-11-06 Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. |
| elegant scipy: Elegant SciPy Juan Nunez-Iglesias. Stéfan van der Walt. Harriet Dashnow, 2017 |
| elegant scipy: Effective Computation in Physics Anthony Scopatz, Kathryn D. Huff, 2015-06-25 More physicists today are taking on the role of software developer as part of their research, but software development isnâ??t always easy or obvious, even for physicists. This practical book teaches essential software development skills to help you automate and accomplish nearly any aspect of research in a physics-based field. Written by two PhDs in nuclear engineering, this book includes practical examples drawn from a working knowledge of physics concepts. Youâ??ll learn how to use the Python programming language to perform everything from collecting and analyzing data to building software and publishing your results. In four parts, this book includes: Getting Started: Jump into Python, the command line, data containers, functions, flow control and logic, and classes and objects Getting It Done: Learn about regular expressions, analysis and visualization, NumPy, storing data in files and HDF5, important data structures in physics, computing in parallel, and deploying software Getting It Right: Build pipelines and software, learn to use local and remote version control, and debug and test your code Getting It Out There: Document your code, process and publish your findings, and collaborate efficiently; dive into software licenses, ownership, and copyright procedures |
| elegant scipy: Python Machine Learning Sebastian Raschka, 2015-09-23 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Keras and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models. |
| elegant scipy: Introduction to Scientific Programming with Python Joakim Sundnes, 2020-07-01 This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies. |
| elegant scipy: Data Structures and Algorithms with Python Aadinath Pothuvaal, 2025-02-20 Dive into the Heart of Pythonic Algorithms and Data Structures offers a comprehensive guide designed to empower both beginners and seasoned developers. Whether you're mastering the foundations of computer science or enhancing your problem-solving skills, this book provides a roadmap through the intricacies of efficient data organization and algorithmic prowess. We introduce the versatility of Python, setting the stage for an exploration of various data structures, including arrays, linked lists, stacks, queues, trees, and graphs. Each chapter presents practical examples and Python code snippets for easy comprehension and application. As the journey progresses, we shift focus to algorithms, covering sorting techniques, searching methods, and dynamic programming. Real-world applications and case studies bridge the gap between theory and practical implementation, reinforcing each algorithm's relevance in solving tangible problems. The book emphasizes a hands-on approach, encouraging active engagement with Python code and algorithms. Whether you're preparing for coding interviews, building scalable software, or honing your programming skills, this book equips you with the knowledge and confidence to navigate the challenging terrain of Data Structures and Algorithms using Python. |
| elegant scipy: Mastering matplotlib Duncan M. McGreggor, 2015-06-29 matplotlib is a Python plotting library that provides a large feature set for a multitude of platforms. Given the depth of the library's legacy and the variety of related open source projects, gaining expert knowledge can be a time-consuming and often confusing process. You'll begin your exciting journey learning about the skills that are necessary in leading technical teams for a visualization project or to become a matplotlib contributor. Supported by highly-detailed IPython Notebooks, this book takes you through the conceptual components underlying the library and then provides a detailed overview of its APIs. From there, you will learn about event handling and how to code for interactive plots. Next you will move on to customization techniques, local configuration of matplotib, and then deployments in Cloud environments. The adventure culminates in an exploration of big data visualization and matplotlib clustering. |
| elegant scipy: Learning Scientific Programming with Python Christian Hill, 2016-02-04 Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming. |
| elegant scipy: 차근차근 딥 러닝 코딩 실습 조민수 지음 ┃ 김춘지 기획, 2020-12-18 딥 러닝은 2006년에 토론토 대학의 제프리 힌톤 박사가 고안했다. 딥 러닝이 세상에 알려진 것은 2012년에 구글이 “대규모 비지도 학습 이미지 데이터를 기반으로 기계가 고양이를 자동으로 인식할 수 있다”라고 발표하면서부터이다. 딥 러닝이 사회적으로 널리 알려진 것은 2016년에 구글의 알파고가 이세돌 9단과의 바둑 시합에서 승리하면서이다. 딥 러닝은 중간 레이어가 여러 개로 구성된 신경망을 사용해 구축하므로 심층 신경망 (Deep Neural Network: DNN)이라고 부르기도 한다. 딥 러닝은 분류와 회귀 문제에 적용 가능하다. 신경망 알고리즘은 인간 뇌의 뉴런이 자극을 전달하는 방법에서 영감을 받아 만들어 졌고, 딥 러닝과 신경망이라는 용어는 구분하지 않고 사용하는 경우가 많다. 그러나 딥 러닝은 실제 인간 뇌가 작동하는 방식과 같지 않으며 직접적으로 연관이 있지 않다. 딥 러닝은 신경망에서 발전된 것으로 머신 러닝 알고리즘의 하나이지만 기존의 머신 러닝과는 완전히 다른 학습 방법이라고 보는 시각도 있다. 그 이유는 특징을 추출할 때 사람이 개입할 필요가 없기 때문이다. 기존의 머신 러닝은 특징 추출을 사람이 직접 해야 했지만, 딥 러닝은 기계가 학습하면서 자동으로 특징을 추출한다. |
| elegant scipy: The Hitchhiker's Guide to Python Kenneth Reitz, Tanya Schlusser, 2016-08-30 The Hitchhiker's Guide to Python takes the journeyman Pythonista to true expertise. More than any other language, Python was created with the philosophy of simplicity and parsimony. Now 25 years old, Python has become the primary or secondary language (after SQL) for many business users. With popularity comes diversityâ??and possibly dilution. This guide, collaboratively written by over a hundred members of the Python community, describes best practices currently used by package and application developers. Unlike other books for this audience, The Hitchhikerâ??s Guide is light on reusable code and heavier on design philosophy, directing the reader to excellent sources that already exist. |
| elegant scipy: SciPy Programming Succinctly James McCaffrey, 2017-02-01 James McCaffrey's SciPy Programming Succinctly offers readers a quick, thorough grounding in knowledge of the Python open source extension SciPy. The SciPy library, accompanied by its interdependent NumPy, offers Python programmers advanced functions that work with arrays and matrices. Each section presents a complete demo program for programmers to experiment with, carefully chosen examples to best illustrate each function, and resources for further learning. Use this e-book to install and edit SciPy, and use arrays, matrices, and combinatorics in Python programming. |
| elegant scipy: Data Science from Scratch Joel Grus, 2015-04-14 This is a first-principles-based, practical introduction to the fundamentals of data science aimed at the mathematically-comfortable reader with some programming skills. The book covers: The important parts of Python to know The important parts of Math / Probability / Statistics to know The basics of data science How commonly-used data science techniques work (learning by implementing them) What is Map-Reduce and how to do it in Python Other applications such as NLP, Network Analysis, and more. |
| elegant scipy: Python Programming Guide. Exploring Computer Programming Sheetal Thakare, 2020-08-18 Document from the year 2020 in the subject Computer Science - Programming, , course: Python Programming, language: English, abstract: This Guide is explaining Python as it is the the perfect language for beginners trying to explore computer programming for the first time. Python is a general-purpose language. It has wide range of applications from web development to scientific and mathematical computing to desktop Graphical User Interfaces. The syntax of the language is clean and length of the code is relatively short. It's fun to work in Python because it allows you to think about the problem rather than focusing on the syntax. |
| elegant scipy: Text Analytics with Python Dipanjan Sarkar, 2016-11-30 Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Builda text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern Who This Book Is For : IT professionals, analysts, developers, linguistic experts, data scientists, and anyone with a keen interest in linguistics, analytics, and generating insights from textual data |
| elegant scipy: Python Tutorials - Herong's Tutorial Examples Herong Yang, 2011-04-01 This Python tutorial book is a collection of notes and sample codes written by the author while he was learning Python language himself. Topics include: installing Python environments on Windows, macOS and Linux computer; Python built-in data types; variables, operations, expressions and statements; user-defined functions; iterators, generators and list/set/dictionary comprehensions; modules and packages; sys, os, and pathlib modules; Anaconda Python environment manager; Jupyter Notebooks; NumPy, SciPy libraries. Updated in 2024 (Version v2.15) with minor changes. For latest updates and free sample chapters, visit https://www.herongyang.com/Python. |
| elegant scipy: Beginning Programming with Python For Dummies John Paul Mueller, 2018-02-13 The easy way to learn programming fundamentals with Python Python is a remarkably powerful and dynamic programming language that's used in a wide variety of application domains. Some of its key distinguishing features include a very clear, readable syntax, strong introspection capabilities, intuitive object orientation, and natural expression of procedural code. Plus, Python features full modularity, supporting hierarchical packages, exception-based error handling, and modules easily written in C, C++, Java, R, or .NET languages, such as C#. In addition, Python supports a number of coding styles that include: functional, imperative, object-oriented, and procedural. Due to its ease of use and flexibility, Python is constantly growing in popularity—and now you can wear your programming hat with pride and join the ranks of the pros with the help of this guide. Inside, expert author John Paul Mueller gives a complete step-by-step overview of all there is to know about Python. From performing common and advanced tasks, to collecting data, to interacting with package—this book covers it all! Use Python to create and run your first application Find out how to troubleshoot and fix errors Learn to work with Anaconda and use Magic Functions Benefit from completely updated and revised information since the last edition If you've never used Python or are new to programming in general, Beginning Programming with Python For Dummies is a helpful resource that will set you up for success. |
| elegant scipy: 実践ソフトウェアエンジニアリング（第9版） Roger S. Pressman, Bruce R. Maxim, 西康晴, 水野昇幸, 池田暁, 井芹久美子, 井芹洋輝, 岡澤裕二, 金子昌永, 衣笠駿, 鈴木一裕, 根本紀之, 松尾和昭, 山﨑崇, 2021-12-01 ソフトウェアエンジニアリング・スタンダードの第9版 「ソフトウェア技術者なら、この財産を活用しない手はない」とマイクロソフト榊原彰氏よりの推薦文にあるように、その通りに期待できる内容です。 「本書は米国においての第1版が発行（1982年）されて以来、世界累積300万部を超えるベストセラーの最新刊である第9版の邦訳書です。ソフトウェア同様、改良が続けられているソフトウェアエンジニアリングの「最良の手法」を解説している書籍であり、現役のソフトウェアエンジニアならびに学生諸氏におすすめする1冊です。 原書：Roger S. Pressman, Bruce R. Maxim, Software Engineering,McGraw-Hill, 2020 このような方におすすめ ソフトウェアエンジニア、大学院生、学生 主要目次 著者について／まえがき／訳者まえがき 第1章 ソフトウェアとソフトウェアエンジニアリング 第1部 ソフトウェアプロセス 第2章 プロセスモデル 第3章 アジャイルとプロセス 第4章 推奨のプロセスモデル 第5章 ソフトウェアエンジニアリングの人間的側面 第2部 モデリング 第6章 プラクティスの指針となる原則 第7章 要求エンジニアリング 第8章 要求モデリングの推奨手法 第9章 設計の概念 第10章 アーキテクチャ設計の推奨手法 第11章 コンポーネント設計 第12章 ユーザエクスペリエンス設計 第13章 移動体端末におけるソフトウェアの設計 第14章 パターンに基づく設計 第3部 品質とセキュリティ 第15章 品質の概念 第16章 レビューの推奨手法 第17章 ソフトウェア品質保証 第18章 ソフトウェアセキュリティエンジニアリング 第19章 ソフトウェアテスト―コンポーネントレベル 第20章 ソフトウェアテスト―統合レベル 第21章 ソフトウェアテスト―移動体端末と特定ドメインに対するテスト 第22章 ソフトウェア構成マネジメント 第23章 ソフトウェアメトリクスと分析 第4部 ソフトウェアプロジェクトのマネジメント 第24章 プロジェクトマネジメントの概念 第25章 実行可能で役立つソフトウェア計画 第26章 リスクマネジメント 第27章 ソフトウェアサポート戦略 第5部 先端的な話題 第28章 ソフトウェアプロセス改善 第29章 ソフトウェアエンジニアリングの新興トレンド 第30章 おわりに 付録／参考文献／索引／訳者プロフィール |
| elegant scipy: Feature Engineering for Machine Learning Alice Zheng, Amanda Casari, 2018-03-23 Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques |
| elegant scipy: Programming Python Mark Lutz, 1996 This handbook describes how to use Python, an increasingly popular object-oriented scripting language freely available over the Net. Python is an interpreted language, useful for quick prototyping and simple programs for which C++ is too complex and unwieldy. The Python interpreter is available on most popular UNIX platforms, including Linux, as well as Windows and the Mac. |
| elegant scipy: Introduction to Python in Earth Science Data Analysis Maurizio Petrelli, 2021-09-16 This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book. |
ELEGANT Synonyms: 160 Similar and Opposite Words - Merriam-Webster
Some common synonyms of elegant are choice, dainty, delicate, exquisite, and rare. While all these words mean "having qualities that appeal to a cultivated taste," elegant applies to what is rich …
ELEGANT | English meaning - Cambridge Dictionary
ELEGANT definition: 1. graceful and attractive in appearance or behaviour: 2. An elegant idea, plan, or solution is…. Learn more.
elegant adjective - Definition, pictures, pronunciation and usage …
Definition of elegant adjective from the Oxford Advanced Learner's Dictionary. (of people or their behaviour) attractive and showing a good sense of style synonym stylish. She was tall and …
Elegant - definition of elegant by The Free Dictionary
elegant - refined and tasteful in appearance or behavior or style; "elegant handwriting"; "an elegant dark suit"; "she was elegant to her fingertips"; "small churches with elegant white spires"; "an …
ELEGANT definition and meaning | Collins English Dictionary
If you describe a person or thing as elegant, you mean that they are pleasing and graceful in appearance or style.
Elegant Definition & Meaning - YourDictionary
Elegant definition: Characterized by or exhibiting refined, tasteful beauty of manner, form, or style.
Elegant - Definition, Meaning & Synonyms - Vocabulary.com
Very stylishly dressed men and women are elegant, and fine restaurants with distinctive delicacies are elegant. Most ordinary places and people can become elegant, though, when dressed up or …
What does Elegant mean? - Definitions.net
Elegance is beauty that shows unusual effectiveness and simplicity. Elegance is frequently used as a standard of tastefulness, particularly in visual design, decorative arts, literature, science, and …
ELEGANT Definition & Meaning - Merriam-Webster
The meaning of ELEGANT is marked by elegance. How to use elegant in a sentence. Synonym Discussion of Elegant.
Meaning of elegant – Learner’s Dictionary - Cambridge Dictionary
ELEGANT definition: stylish or attractive in appearance or behaviour: . Learn more.
ELEGANT Synonyms: 160 Similar and Opposite Words - Merriam-Webster
Some common synonyms of elegant are choice, dainty, delicate, exquisite, and rare. While all these words mean "having qualities that appeal to a cultivated taste," elegant applies to what is …
ELEGANT | English meaning - Cambridge Dictionary
ELEGANT definition: 1. graceful and attractive in appearance or behaviour: 2. An elegant idea, plan, or solution is…. Learn more.
elegant adjective - Definition, pictures, pronunciation and usage …
Definition of elegant adjective from the Oxford Advanced Learner's Dictionary. (of people or their behaviour) attractive and showing a good sense of style synonym stylish. She was tall and …
Elegant - definition of elegant by The Free Dictionary
elegant - refined and tasteful in appearance or behavior or style; "elegant handwriting"; "an elegant dark suit"; "she was elegant to her fingertips"; "small churches with elegant white …
ELEGANT definition and meaning | Collins English Dictionary
If you describe a person or thing as elegant, you mean that they are pleasing and graceful in appearance or style.
Elegant Definition & Meaning - YourDictionary
Elegant definition: Characterized by or exhibiting refined, tasteful beauty of manner, form, or style.
Elegant - Definition, Meaning & Synonyms - Vocabulary.com
Very stylishly dressed men and women are elegant, and fine restaurants with distinctive delicacies are elegant. Most ordinary places and people can become elegant, though, when dressed up …
What does Elegant mean? - Definitions.net
Elegance is beauty that shows unusual effectiveness and simplicity. Elegance is frequently used as a standard of tastefulness, particularly in visual design, decorative arts, literature, science, …
ELEGANT Definition & Meaning - Merriam-Webster
The meaning of ELEGANT is marked by elegance. How to use elegant in a sentence. Synonym Discussion of Elegant.
Meaning of elegant – Learner’s Dictionary - Cambridge Dictionary
ELEGANT definition: stylish or attractive in appearance or behaviour: . Learn more.
