Название: Машинное обучение с PyTorch и Scikit-Learn
Автор: Лю Юси (Хэйден), Мирджалили Вахид
Издательство: бхв
Год: 2023
Формат: PDF
Страниц: 690
Размер: 85 Mb
Язык: Русский

Исчерпывающее руководство по машинному (МО) и глубокому обучению с использованием языка программирования Python, фреймворка PyTorch и библиотеки scikit-learn. Рассмотрены основы МО, алгоритмы для задач классификации, классификаторы на основе scikit-learn, предварительная обработка и сжатие данных, современные методы оценки моделей и объединение различных моделей для ансамблевого обучения.

Название: Введение в статистическое обучение с примерами на Python
Автор: Джеймс Г., Уиттон Д., Хасти Т., Тибширани Р.
Издательство: дмк
Год: 2024
Формат: PDF
Страниц: 848
Размер: 21 Mb
Язык: Русский

Книга доступным для восприятия языком описывает все разнообразие форм статистического обучения – полезного инструментария для извлечения выводов из огромных наборов данных, появившихся в последние 20 лет в самых разных областях науки. В дополнение к линейной регрессии описываются многие из наиболее значимых на сегодняшний день подходов в статистике и машинном обучении, включая методы повторной выборки, разреженные методы классификации и регрессии, обобщенные аддитивные модели, методы на основе деревьев, машины опорных векторов, глубокое обучение, анализ выживаемости или надежности, кластеризацию и множественную проверку гипотез. Повествование в книге обогащается примерами из реальной жизни. Книга предназначена не только для опытных специалистов в области статистики, но и для тех, кто желает попробовать применить продвинутые техники статистического обучения при анализе своих данных.

Название: Neural Networks for Beginners: Unlock the Secrets of Neural Networks. A Beginner's Guide to AI's Most Powerful Tool
Автор: James Ferry
Издательство: Independently published
Год: 2024
Страниц: 102
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

"Neural Networks for Beginners: Unlock the Secrets of Neural Networks" is your essential guide to understanding and mastering one of artificial intelligence's most powerful tools. Whether you're new to the world of neural networks or looking to deepen your understanding, this beginner-friendly book demystifies complex concepts and empowers you to harness the full potential of neural networks. From the fundamentals of neural network architecture to practical applications in image recognition, natural language processing, and beyond, this book covers everything you need to know to get started. You'll learn about neurons, layers, activation functions, and the training process, gaining a solid foundation in the building blocks of neural networks. With step-by-step instructions and real-world examples, you'll discover how to implement neural networks using popular Python libraries like TensorFlow and Keras. Whether you're building your first neural network or exploring advanced techniques, this book provides clear guidance and hands-on exercises to help you succeed. But Neural Networks for Beginners goes beyond the basics. It also explores the challenges and limitations of neural networks, including data quality, interpretability, and ethical considerations. Plus, you'll get a glimpse into the future of neural networks, with insights into emerging trends like explainable AI and quantum neural networks.

Название: Scientific Computing with Python: Mastering Numpy and Scipy
Автор: John Smith
Издательство: HiTeX Press
Год: 2024
Страниц: 348
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

"Scientific Computing with Python: Mastering Numpy and Scipy" is a comprehensive guide designed to equip readers with the knowledge and skills necessary for efficient numerical computations and data analysis. Whether you're a beginner or an advanced user, this book delves into essential topics such as array manipulation, advanced Numpy techniques, and the vast functionalities of Scipy, including optimization, linear algebra, signal processing, and statistical analysis. Each chapter builds on the previous one, offering detailed explanations, practical examples, and best practices. With an emphasis on real-world applications and case studies, this book is an invaluable resource for researchers, engineers, data scientists, and educators aiming to excel in the field of scientific computing. Discover the power of Python's robust libraries and elevate your computational skills to solve complex scientific problems. The content of this book is meticulously structured into chapters, each focusing on an essential and unique topic. The initial chapters introduce the role of Python in scientific computing, followed by getting started with Numpy and moving on to more advanced Numpy techniques. Subsequent chapters provide an introduction to Scipy, explore linear algebra, optimization, integration, differentiation, signal processing, and statistics using Scipy. The final chapter is dedicated to practical applications and case studies, demonstrating the real-world use of the concepts and techniques discussed.

Название: Computation and Simulation for Finance: An Introduction with Python
Автор: Cónall Kelly
Издательство: Springer
Год: 2024
Страниц: 330
Язык: английский
Формат: pdf (true), epub
Размер: 31.3 MB

This book offers an up-to-date introductory treatment of computational techniques applied to problems in finance, placing issues such as numerical stability, convergence and error analysis in both deterministic and stochastic settings at its core. The first part provides a welcoming but nonetheless rigorous introduction to the fundamental theory of option pricing, including European, American, and exotic options along with their hedge parameters, and combines a clear treatment of the mathematical framework with practical worked examples in Python. The second part explores the main computational methods for valuing options within the Black-Scholes framework: lattice, Monte Carlo, and finite difference methods. The third and final part covers advanced topics for the simulation of financial processes beyond the standard Black-Scholes setting. Techniques for the analysis and simulation of multidimensional financial data, including copulas, are covered and will be of interest to those studying Machine Learning for finance. There is also an in-depth treatment of exact and approximate sampling methods for stochastic differential equation models of interest rates and volatilities. Written for advanced undergraduate and masters-level courses, the book assumes some exposure to core mathematical topics such as linear algebra, ordinary differential equations, multivariate calculus, probability, and statistics at an undergraduate level. While familiarity with Python is not required, readers should be comfortable with basic programming constructs such as variables, loops, and conditional statements. We choose Python as our language of instruction, reflecting its growing usage among quantitative analysts. Experience with Python is not assumed, though readers should be familiar with basic programming constructs such as variables, loops, and conditional statements.

Название: Optimized Computational Intelligence Driven Decision-Making: Theory, Application and Challenges
Автор: Hrudaya Kumar Tripathy, Sushruta Mishra, Minakhi Rout, S. Balamurugan
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 368
Язык: английский
Формат: epub
Размер: 12.5 MB

This book covers a wide range of advanced techniques and approaches for designing and implementing computationally intelligent methods in different application domains which is of great use to not only researchers but also academicians and industry experts. Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, Artificial Intelligence (AI), cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains. The book will interest a range of engineers and researchers in information technology, Computer Science, and Artificial Intelligence working in the interdisciplinary field of Computational Intelligence.

Название: Monte Carlo Simluations for Options: Unlocking Precision in Financial Forecasting With Python
Автор: Hayden Van Der Post
Издательство: Reactive Publishing
Год: 2024
Страниц: 508
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Unlock the power of advanced financial modeling with "Monte Carlo Simulations for Options" – the ultimate guide for enthusiasts ready to elevate their understanding of financial derivatives. This book is the ideal follow-up for those who already have a foundation in option pricing and are eager to dive into more sophisticated and nuanced techniques. Navigate through the intricate world of Monte Carlo simulations applied to financial markets with this comprehensive and accessible resource. Discover how to harness the computational might of Python to create robust models, simulate various market scenarios, and accurately price complex options. In selecting Python as the programming language for this book, we considered its widespread use, readability, and extensive library support for scientific computing. Python’s burgeoning role in finance, data science, and machine learning makes it an ideal tool for implementing Monte Carlo simulations. Throughout the book, you'll find code snippets, examples, and exercises designed to provide hands-on experience and reinforce the concepts covered.

Название: ReactJS Absolute Beginners: A Complete Guide React JS Tutorial For Beginners Step By Step With Examples
Автор: Kavi & Shila
Издательство: Kavi & Shila Book Publication House
Год: 2024
Страниц: 722
Язык: английский
Формат: epub
Размер: 11.7 MB

ReactJS Absolute Beginners is your comprehensive guide to mastering this powerful jаvascript library for building dynamic and user-friendly web applications. Even if you have no prior coding experience, this book will equip you with the fundamental knowledge and hands-on practice you need to become a confident React developer. ReactJS is a free and open-source front-end jаvascript library which is used to develop various interactive user-interfaces. It is a simple, feature rich and component based UI library. When we say component based, we mean that React develops applications by creating various reusable and independent codes. This UI library is, thus, widely used in web development. ReactJS can be used to develop small applications as well as big, complex applications. ReactJS provides a minimal and solid feature set to kick-start a web application. React community compliments React library by providing a large set of ready-made components to develop web applications in a record time. The React community also provides advanced concepts like state management, routing, etc., on top of the React library.

Название: AI Powered Financial Analysis: Harnessing Artificial Intelligence for Financial Analysis and Market Insights with Python
Автор: Hayden Van Der Post
Издательство: Reactive Publishing
Год: 2024
Страниц: 418
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Unlock the Future of Finance with AI! Step into the cutting-edge world of Artificial Intelligence (AI) and take your investment strategies to the next level with Artificial Intelligence. Are you an avid market player who mastered the basics and now craves more sophisticated techniques? In this compelling guide, you'll delve deep into AI's transformative potential within the realm of finance. From crafting advanced predictive models to uncovering hidden patterns in big data, this book empowers you to harness AI's immense capabilities, no matter your starting point. Discover insider secrets used by industry-leading analysts as you explore real-world applications, case studies, and practical methodologies. Unleash AI-driven algorithms that elevate your market insights, accelerating your journey from an enthusiast to a frontrunner in financial forecasting and decision-making. Languages like Python, with libraries such as Pandas and NumPy, and platforms like QuantConnect and Algorithmic Trading Software (ATS), have become essential in designing, backtesting, and deploying these trading algorithms. Whether you're enhancing your portfolio strategies or driving innovation in financial services, "AI Powered" demystifies the complexities of Artificial Intelligence, presenting them in a user-friendly format enriched with expert tips. Equip yourself with the tools to achieve unprecedented accuracy, agility, and success in the rapidly evolving financial landscape. This book is designed for financial professionals, data scientists, and anyone keen on navigating the promising yet complex waters of AI in finance. Whether you’re a seasoned analyst, a portfolio manager, or a curious newcomer, our goal is to arm you with the knowledge, tools, and insights necessary to leverage AI to its fullest potential.

Название: R-ticulate: A Beginner's Guide to Data Analysis for Natural Scientists
Автор: Martin Bader, Sebastian Leuzinger
Издательство: Wiley
Год: 2024
Страниц: 224
Язык: английский
Формат: epub
Размер: 28.2 MB

An accessible learning resource that develops data analysis skills for natural science students in an efficient style using the R programming language. R-ticulate: A Beginner’s Guide to Data Analysis for Natural Scientists is a compact, example-based, and user-friendly statistics textbook without unnecessary frills, but instead filled with engaging, relatable examples, practical tips, online exercises, resources, and references to extensions, all on a level that follows contemporary curricula taught in large parts of the world. The content structure is unique in the sense that statistical skills are introduced at the same time as software (programming) skills in R. This is by far the best way of teaching from the authors’ experience. Our aim was to produce a surmountable, paced, 12‐chapter text which suits the typical 12 week (1 semester) layout of many undergraduate courses. Just like in our classroom teaching, we use easy‐to‐understand language to explain key concepts and strive to strike a balance between the practical application of statistical models and a conceptual understanding of the theoretical foundations. The content of the chapters can be used selectively, and we deliberately go deeper where we deem detail is useful.We demonstrate the use of base R as well as that of more recent developments (Tidyverse) as we firmly believe that both have their place, and taking the best of both worlds makes most sense. Exercises and datasets are available online to save space and keep the volume of the text ‘light’, particularly for those who prefer to work with a hard copy. We hope this text will help you to R-ticulate yourself well in a data-driven world where statistical analysis and modelling skills are ever more important and in high demand with employers, far beyond the natural sciences.