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Название: Python Data Science Handbook: Essential Tools for Working with Data, 2nd Edition (Final Release)
Автор: Jake VanderPlas
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 591
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 30.8 MB

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find the second edition of this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

Название: Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines (Final Release)
Автор: Yada Pruksachatkun, Matthew McAteer, Subhabrata Majumdar
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 303
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 47.0 MB

With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable. This book is written for anyone who is currently working with Machine Learning models and wants to be sure that the fruits of their labor will not cause unintended harm when released into the real world. The primary audience of the book are engineers and data scientists who have some familiarity with Machine Learning. Parts of the book should be accessible to non-engineers, such as product managers and executives with a conceptual understanding of ML. Some of you may be building ML systems that make higher-stakes decisions than they encountered in your previous job or in academia. We assume you are are familiar with the very basics of Deep Learning, and Python for the code samples.

Название: A Student's Guide to Python for Physical Modeling, 2nd Edition
Автор: Jesse M. Kinder, Philip Nelson
Издательство: Princeton University Press
Год: 2021
Страниц: 241
Язык: английский
Формат: pdf (true)
Размер: 10.3 MB

A fully updated tutorial on the basics of the Python programming language for science students. Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for Data Science and Machine Learning (Pandas and Sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

Название: Machine Learning under Resource Constraints, Volume 3: Applications
Автор: Katharina Morik, Jorg Rahnenfuhrer, Christian Wietfeld
Издательство: De Gruyter
Год: 2023
Страниц: 478
Язык: английский
Формат: pdf (true), epub
Размер: 90.8 MB

Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Volume 3 describes how the resource-aware Machine Learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how Machine Learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine Learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from Machine Learning, for example by uncovering hidden characteristics of the wireless channel.

Название: Machine Learning under Resource Constraints, Volume 2: Discovery in Physics
Автор: Katharina Morik, Wolfgang Rhode
Издательство: De Gruyter
Год: 2023
Страниц: 364
Язык: английский
Формат: pdf (true), epub
Размер: 56.9 MB

Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Volume 2 covers Machine Learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, Machine Learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by Machine Learning.

Название: Machine Learning under Resource Constraints, Volume 1: Fundamentals
Автор: Katharina Morik, Peter Marwedel
Издательство: De Gruyter
Год: 2023
Страниц: 506
Язык: английский
Формат: pdf (true), epub
Размер: 45.7 MB

Machine Learning under Resource Constraints addresses novel Machine Learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel Machine Learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to Machine Learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness.

Название: Разработка IoT для ESP32 и ESP8266 с помощью jаvascript: Практическое руководство по XS и Moddable SDK
Автор: П.Ходди, Л.Прадер
Издательство: Самиздат
Год: 2020
Страниц: 607
Язык: русский
Формат: pdf
Размер: 18.3 MB

Здесь представлен перевод книги Р .Hoddie, L. Prader - IoT Development for ESP32 and ESP8266 with jаvascript - 2020. Эта книга представляет собой практическое руководство по написанию программ для продуктов IoT. Каждая глава заполнена компактными, сфокусированными примерами, на которых вы можете учиться, изучать, запускать и изменять. Когда вы прочтете эту книгу, вы будете знать основы создания сложных продуктов IoT на недорогом оборудовании с использованием современного jаvascript.

Название: Design Patterns with Java: An Introduction
Автор: Olaf Musch
Издательство: Springer
Год: 2023
Страниц: 344
Язык: английский
Формат: pdf (true), epub
Размер: 16.6 MB

A practical description of the software design patterns as they are mentioned in the 1994 book "Design Patterns - Elements of Reusable ObjectOriented Software" by the author group Gamma, Helm, Johnson and Vlissides (also called "Gang of Four", GoF for short). All patterns are explained in detail by means of examples and also critically appreciated. Furthermore, design principles of object-oriented programming are described and considered. All examples are commented in detail in the source code and are executable under Java 16. In part, newer features of Java up to and including version 16 are also explained and used.

Название: Офисное программирование: учебное пособие
Автор: Железко Б. А. , Новицкая Е. Г. , Подгорная Г. Н.
Издательство: РИПО
Год: 2017
Cтраниц: 100 с. : ил.
Формат: pdf (ocr) / djvu
Размер: 11 мб
Язык: русский

В учебном пособии рассматриваются вопросы основ офисного программирования в MS Office. Излагаются технологии работы с языком объектно-ориентированного программирования Visual Basic for Application (VBA). Даются подробное разъяснение структуры элементов программирования языка VBA, краткие теоретические сведения, необходимые для освоения рассматриваемой логики языка программирования, и комплексы заданий, выполняемых на примере различных предметных областей.

Название: Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems (Final Release)
Автор: Hala Nelson
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 605
Язык: английский
Формат: True/Retail PDF EPUB
Размер: 40.8 MB

Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field.