Автор: Андрей Колесов
Название: Советы тем кто программирует на Visual Basic и MS Office-VBA
Издательство: М:, "КомпьютерПресс",
Год: 1996-2002
Страниц: 407
Формат: DJVU, PDF
Размер: 10 МБ

Когда в январе 1996 года я отправлял эту статью в "КомпьютерПресс", то никак не думал, что будет написан даже четвертый совет. Сегодня (май 2000 года) их число уже приблизилось к тремстам... Автор

Автор: Волчёнков Н. Г.
Название: Программирование на Visual Basic 6. В 3-х книгах
Издательство: М:, ИНФРА-М
Год: 2000
Страниц: 288+280+238
Формат: DJVU, PDF
Размер: 44 МБ
«Программирование на Visual Basic 6» — это мини-серия из трёх книг (два учебных пособия и задачник). Первая книга ознакомит Вас с основными понятиями и приёмами программирования, а главное — научит создавать собственные полноценные программы в системе Visual Basic 6. Вторая книга научит Вас, как работать с файлами и базами данных, как создавать меню и линейки инструментов, анимацию и мультимедиа (звуковое сопровождение), как взаимодействовать с сетью Internet. И всё это — с помощью Visual Basic 6. Третья книга...

Название: Building Recommendation Systems in Python and JAX: Hands-On Production Systems at Scale (Final)
Автор: Bryan Bischof, Hector Yee
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 355
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.3 MB, 10.1 MB

Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way. In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, and Weights & Biases. Modern recommendation system (often abbreviated RecSys) designs are as diverse as the domains they serve. RecSys consist of the computer software architectures to implement and execute such product goals in addition to the algorithmic components of ranking. Methods for ranking recommendions can come from traditional statistical learning algorithms, linear algebraic inspirations, geometric considerations, and, of course, gradient based methods. Just as the algorithmic methods are diverse, so too are the modeling and evaluation considerations for recommending: personalized ranking, search recommendations, sequence modeling, and the scoring for all of the above are now need-to-know for the working ML Engineer in the space of recommendation systems.

Название: Python 3 Data Visualization Using ChatGPT / GPT-4
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 314
Язык: английский
Формат: pdf (true)
Размер: 20.4 MB

This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.

Название: Python 3 Using ChatGPT / GPT-4
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 202
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book is intended primarily for people who want to learn both Python 3 and how to use ChatGPT with Python. Chapter One begins with an introduction to fundamental aspects of Python programming, including various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. Later, the book covers loops, conditional logic, and reserved words in Python. You will also see how to handle user input, manage exceptions, and work with command-line arguments. Next, the text transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of ChatGPT, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via ChatGPT using the "Code Interpreter" plugin. Code samples and figures from the book are available for downloading. In essence, the book provides a modest bridge between the worlds of Python programming and AI, aiming to equip readers with the knowledge and skills to navigate both domains confidently.

Название: Statistics Using Python
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 273
Язык: английский
Формат: pdf (true)
Размер: 18.0 MB

This book is designed to offer a fast-paced yet thorough introduction to essential statistical concepts using Python code samples, and aims to assist data scientists in their daily endeavors. The ability to extract meaningful insights from data requires a deep understanding of statistics. The book ensures that each topic is introduced with clarity, followed by executable Python code samples that can be modified and applied according to individual needs. Topics include working with data and exploratoryanalysis, the basics of probability, descriptive and inferential statistics and their applications, metrics for data analysis, probability distributions, hypothesis testing, and more. Appendices on Python and Pandas have been included. From foundational Python concepts to the intricacies of statistics, this book serves as a comprehensive resource for both beginners and seasoned professionals.

Название: Python 3 and Feature Engineering
Автор: Oswald Campesato
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 229
Язык: английский
Формат: pdf (true)
Размер: 10.7 MB

This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework.

Название: Productizing Quantum Computing: Bring Quantum Computing Into Your Organization
Автор: Dhairyya Agarwal, Shalini D, Srinjoy Ganguly
Издательство: Apress
Год: 2024
Страниц: 174
Язык: английский
Формат: pdf
Размер: 22.2 MB

Leverage the benefits of Quantum Computing by identifying business use cases and understanding how to design and develop quantum products and services. This book will guide you to effectively productize quantum computing, including best practices, recommendations, and proven methods to help you navigate the challenges and risks of this emerging technology. The book starts with a thorough introduction to quantum computing, followed by its various algorithms and applications. You will then learn how to build a strong foundation in classical computing, seek practical experience, and stay up-to-date with the latest developments in the field. Moving forward, you will gain an understanding of how to conduct market research to identify business opportunities for quantum computing products and services. The authors then guide you through the process of developing a quantum roadmap and integrating quantum computing into an existing system. This is concluded by a demonstration of how to manage quantum computing projects and how to address their risks and challenges. After reading this book, you will understand quantum computing and how it can be applied to real-world business problems. For product managers, developers, and entrepreneurs who wish to use the potential of Quantum Computing for their businesses.

Название: Word 2021 в примерах
Автор: Осипов Е.А., Карчевский Е.М., Филиппов И.Е.
Издательство: Казань: Казанский университет
Год: 2022
Формат: pdf
Страниц: 120
Размер: 12 mb
Язык: Русский

В пособии рассмотрены задачи по созданию, редактированию и обработке текстовых и графических данных с применением текстового редактора Microsoft Word 2021 из пакета прикладного программного обеспечения Microsoft Office 2021 под управлением операционной системы Windows.
Учебное пособие предназначено для студентов вузов, аспирантов и преподавателей.

Название: Molecular Networking: Statistical Mechanics in the Age of AI and Machine Learning
Автор: Caroline Desgranges, Jerome Delhommelle
Издательство: CRC Press
Год: 2024
Страниц: 249
Язык: английский
Формат: pdf (true)
Размер: 26.8 MB

The book builds on the analogy between social groups and assemblies of molecules to introduce the concepts of statistical mechanics, Machine Learning and Data Science. Applying a data analytics approach to molecular systems, we show how individual (molecular) features and interactions between molecules, or "communication" processes, allow for the prediction of properties and collective behavior of molecular systems - just as polling and social networking shed light on the behavior of social groups. Applications to systems at the cutting-edge of research for biological, environmental, and energy applications are also presented. Social networks, Machine Learning, and Artificial Intelligence (AI) have become part of our daily lives. We live in an era where data analysis is present in all aspects of society and a driving force for many decisions that impact our present and future. While statistics have long played a significant role in numbers-driven domains, the development of novel machine learning algorithms, combined with the increase in computing performance and data storage, has led to a paradigm shift in how we approach and address challenges. For instance, in human health, the concept of precision medicine, which considers the individual features of a patient, has emerged as a promising alternative to one-size-fits-all medical treatments. Similarly, the sampling of opinion through polling methods had been for decades a staple of, for example, commercial and political analyses. It is now complemented by the analysis of data from social networks, which provide a window into human interactions and the interrelation between individual and collective responses.