|
 |
|
 |
|
|
 |
|  |
|
 Название: Learn coding with Python and javascript: A practical introduction for beginners
Автор: Joachim L. Zuckarelli
Издательство: Springer
Год: 2024
Страниц: 555
Язык: английский
Формат: pdf (true), epub (true)
Размер: 25.3 MB
Whether on the computer, tablet, mobile phone, in the car or in the coffee machine - computer programs determine our everyday life. Software is becoming increasingly important, hardly anything works without the mysterious power of algorithms. But how do programs work? And how do you develop them? This book teaches you the basics of programming. Using everyday examples, you will first learn the basic concepts of programming, which are similar in all programming languages. Based on these basic ideas, you will then learn two popular and very useful programming languages, Python and javascript, in a systematic way and with many practical exercises, which you can use for a wide range of different tasks. The book is aimed at novice programmers of all ages (from students to professionals) who have no previous programming experience. If you talk to professional software developers, you will often hear that they know a range of languages in which computer programs are written. Some programmers list an impressively long list of languages with sometimes very strange names. How is that possible? How can they all speak two, three, four of these programming languages? Are they all geniuses? Not at all. However, programmers take advantage of a simple fact, namely that programming languages are very similar in many aspects, much more similar than natural languages like English or Spanish are to each other. Many basic concepts are found in virtually all programming languages in one form or another. They may be called differently in each language, but in the end they are always just variants of the same idea implemented in different ways. If you understand these basic concepts, you can learn new programming languages quickly because you know exactly what to look for and only need to understand how the new programming language you’re learning implements each basic concept. This makes language learning much easier. | |
Разместил: Ingvar16 8-09-2024, 19:14 | Комментарии: 0 | Подробнее
| | | |
 |
|  |
|
|
|
|
|
|
| |
|
 Название: Python (2nd Edition) Learn Python in a day and be a professional: This book makes coding with Python easy: Python for Beginners Learn to code with Python
Автор: Basil Uchenna
Издательство: Independently published
Год: 2024
Язык: английский
Формат: epub
Размер: 10.1 MB
Whether you are a beginner eager to dive into the realm of web development or an experienced developer looking to enhance your skills and broaden your knowledge base, these books are designed for you. This book will guide you through the steps required to build a robot web application, not only that, you will learn the requirements needed, the tools, and platforms. Django, a high-level Python web framework, is renowned for its simplicity and efficiency, adhering to the principle of "Don't Repeat Yourself". It empowers developers to build secure, scalable, and maintainable websites swiftly. Python’s clear syntax and Django’s robust framework capabilities make it an ideal choice for developers looking to create powerful web applications with less code. This book is tailored to walk you through the essential concepts, tools, and practices to become proficient in Django. Starting from the basics of the respective languages and frameworks, we will delve into more complex topics, such as database integration, API construction, security enhancements, and application deployment. Practical examples and real-world projects will be integral to our exploration, ensuring that the knowledge you gain is not only theoretical but also applicable. | |
Разместил: Ingvar16 8-09-2024, 17:05 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Logical Foundations Of Computer Science (In 2 Volumes)
Автор: Peter A. Fejer, Dan A. Simovici
Издательство: World Scientific Publishing
Год: 2025
Страниц: 1336
Язык: английский
Формат: pdf (true)
Размер: 16.0 MB
Logic is a foundational mathematical discipline for Computer Science. This unique compendium provides the main ideas and techniques originating from logic. It is divided into two volumes - propositional logic and predicate logic. The volume presents some of the most important concepts starting with a variety of logic formalisms - Hilbert/Frege systems, tableaux, sequents, and natural deduction in both propositional and first-order logic, as well as transformations between these formalisms. Topics like circuit design, resolution, cutting planes, Hintikka sets, paramodulation, and program verification, which do not appear frequently in logic books are discussed in detail. The useful reference text has close to 800 exercises and supplements to deepen understanding of the subject. It emphasizes proofs and overcomes technical difficulties by providing detailed arguments. Computer scientists and mathematicians will benefit from this volume. Many of the fundamental computing concepts were created by logicians. The most famous such concept is the idea of a general-purpose computer, the Turing Machine. Computer programs are written in symbolic languages, e.g., Python, Java, and Lisp, that contain features of logical notations and symbolisms. Through such connections, the study of logic helps in the design of programs. Logic also has a role in the design of new programming languages, and it is essential for work in Artificial Intelligence (AI). | |
Разместил: Ingvar16 8-09-2024, 03:43 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Elements of Data Science, Machine Learning, and Artificial Intelligence Using R
Автор: Frank Emmert-Streib, Salissou Moutari, Matthias Dehmer
Издательство: Springer
Год: 2023
Страниц: 582
Язык: английский
Формат: pdf (true)
Размер: 22.2 MB
The textbook provides students with tools they need to analyze complex data using methods from Data Science, Machine Learning and Artificial Intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of Data Science: Computer Science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples. Regarding useful programming languages, R and Python are very popular today. However, while both provide similar capabilities, there are differences in certain situations. In this book, we prefer R over Python due to its statistical origin. In fact, R was developed to provide a “statistical programming language.” We will see the benefits of this when discussing hypothesis testing (Chap. 10), resampling methods (Chap. 4), and linear regression (Chap. 11), where R provides excellent functionalities. Although this book does not provide an introduction to programming and mathematics, it presents examples in R for the methods from Machine Learning, Artificial Intelligence, and statistics. | |
Разместил: Ingvar16 8-09-2024, 02:26 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Machine Learning Approaches in Financial Analytics
Автор: Leandros A. Maglaras, Sonali Das, Naliniprava Tripathy, Srikanta Patnaik
Издательство: Springer
Год: 2024
Страниц: 485
Язык: английский
Формат: pdf (true), epub
Размер: 53.1 MB
This book addresses the growing need for a comprehensive guide to the application of Machine Learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and Data Science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of Machine Learning in the financial sector responsibly. The financial world has always been a realm of complexity, marked by volatility, uncertainty, and dynamic interconnectedness. Traditional models and tools have often struggled to capture the multifaceted nature of this domain. However, Machine Learning techniques offer a paradigm shift, providing the capability to process vast amounts of data, identify patterns, and generate insights that were previously unimaginable. Throughout the chapters of this book, we explore the fundamental principles of Machine Learning and how they can be applied to tackle a myriad of financial challenges. From predictive modeling, risk assessment, algorithmic trading, portfolio optimization, fraud detection, to customer segmentation, the potential applications are boundless. Object-oriented programming in Python combined with the power of NumPy, Matplotlib and Jupyter fits the bill perfectly for design and visualization in financial engineering. We find that Python combined with Jupyter is not only very well suited for designing and visualizing structured products and examining the impact on pricing as different design elements are tweaked, but it is also amenable to a variety of extensions and integration with other open-source computational finance libraries. | |
Разместил: Ingvar16 7-09-2024, 19:12 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Data-Driven Systems and Intelligent Applications
Автор: Mangesh M. Ghonge, N. Krishna Chaitanya, Pradeep N., Harish Garg, Alessandro Bruno
Издательство: CRC Press
Серия: Intelligent Data-Driven Systems and Artificial Intelligence
Год: 2025
Страниц: 197
Язык: английский
Формат: pdf (true)
Размер: 12.5 MB
This book comprehensively discusses basic data-driven intelligent systems, the methods for processing the data, and cloud computing with Artificial Intelligence (AI). It presents fundamental and advanced techniques used for handling large user data, and for the data stored in the cloud. It further covers data-driven decision-making for smart logistics and manufacturing systems, network security, and privacy issues in cloud computing. The foundation of Machine Learning is the precise use of models and algorithms. Put another way, an algorithm is just a basic procedure for making use of data, either structured or unstructured, to get a result. Concurrently, a Machine Learning model denotes the program–algorithm combination that allows the program to achieve the required objective. Machine Learning models encompass the broader scope of the output generated by algorithms, which are formulas for making predictions. As a result, making the claim that ML models come from Machine Learning algorithms rather than the other way around is technically correct. Viewing the models in Machine Learning will help us comprehend the function of ML algorithms. Presents the advent of Machine Learning, Deep Learning, and reinforcement technique for cloud computing to provide cost-effective and efficient services. The text will be useful for senior undergraduate, graduate students, and academic researchers in diverse fields including electrical engineering, electronics and communications engineering, computer engineering, manufacturing engineering, and production engineering. | |
Разместил: Ingvar16 7-09-2024, 15:47 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Predictive Analytics for Business using R
Автор: Russell R Barton
Издательство: World Scientific Publishing
Год: 2025
Страниц: 464
Язык: английский
Формат: pdf (true)
Размер: 14.5 MB
The fields of mathematical statistics, statistical graphics, Computer Science and operations research have created the rich set of methods now called Analytics. Often analytics is characterized along three poles: descriptive analytics (what do data tell us), predictive analytics (what can be forecast based on the data, and with what certainty), and prescriptive analytics (how can the data inform changes to improve system performance). This book focuses on the second pole, predictive analytics. The areas of predicting a number, a class, and dynamic behavior are distinct, with different methods. This text has three parts based on these areas. Topics in predicting a number include simple and multiple linear regression, transformation of variables, analysis of observational data via cross-validation, the generalized linear model, designed experiments, and Gaussian process and neural network regression. Classification methods include neural networks, logistic regression, k-nearest neighbor, and linear discriminant analysis. Methods for predicting dynamic behavior include trend analysis, time series analysis and discrete-event dynamic simulation. Characterizing prediction uncertainty is a key focus of this text. The text provides analytic methods appropriate to each area, with an explicit process for applying such methods. The text illustrates the application of predictive analytics methods using the R programming language. | |
Разместил: Ingvar16 7-09-2024, 07:46 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Metaheuristics and Reinforcement Techniques for Smart Sensor Applications
Автор: Adwitiya Sinha, Manju, Samayveer Singh
Издательство: CRC Press
Год: 2025
Страниц: 253
Язык: английский
Формат: pdf (true), epub
Размер: 14.5 MB
This book discusses the fundamentals of wireless sensor networks,and the prevailing method and trends of smart sensor applications. It presents analytical modelling to foster the understanding of network challenges in developing protocols for next-generation communication standards. Metaheuristic algorithms are optimization techniques that draw inspiration from natural and abstract concepts to solve complex problems. Unlike exact algorithms, which aim for optimal solutions, metaheuristics prioritize speed and adaptability, making them suitable for addressing computationally challenging problems with large solution spaces. These algorithms play a vital role in various fields, including combinatorial optimization, Machine Learning, and operations research. In the realm of WSNs, metaheuristic algorithms are instrumental in optimizing routing protocols. WSNs comprise nodes with limited computational resources, energy constraints, and often operate in dynamic environments. Efficient data routing in WSNs is critical for conserving energy, extending network lifetime, and ensuring reliable data delivery. Genetic Algorithms (GA) for CH selection play a pivotal role in the efficiency and performance of wireless sensor networks. The GA algorithm employs evolutionary principles to strategically choose CHs that are responsible for efficient and reliable data transmission in the network. | |
Разместил: Ingvar16 7-09-2024, 06:29 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Обработка естественного языка с TensorFlow
Автор: Ганегедара Т.
Издательство: ДMK
Год: 2020
Cтраниц: 382
Формат: pdf (ocr)
Размер: 17 мб
Язык: русский
Обработка естественного языка применяется в различных приложениях машинного обучения, а TensorFlow является важнейшей библиотекой для реализации систем глубокого обучения на практике. Эта книга знакомит читателя с методами обработки естественного языка и содержит практическое руководство по работе с TensorFlow, предоставляя мощный инструмент для работы с огромными объемами неструктурированных данных и решения уникальных задач по обработке естественного языка. | |
Разместил: rivasss 6-09-2024, 21:38 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
|
|
|
|
|
| |
|
 Название: Embedded Artificial Intelligence: Principles, Platforms and Practices
Автор: Bin Li
Издательство: Springer
Год: 2024
Страниц: 262
Язык: английский
Формат: pdf (true), epub
Размер: 41.6 MB
This book focuses on the emerging topic of Embedded Artificial Intelligence and provides a systematic summary of its principles, platforms, and practices. In the section on principles, it analyzes three main approaches for implementing Embedded Artificial Intelligence: cloud computing mode, local mode, and local-cloud collaborative mode. The book identifies five essential components for implementing Embedded Artificial Intelligence: embedded AI accelerator chips, lightweight neural network algorithms, model compression techniques, compiler optimization techniques, and multi-level cascaded application frameworks. The platform section introduces mainstream embedded AI accelerator chips and software frameworks currently used in the industry. The practical part outlines the development process of Embedded Artificial Intelligence and showcases real-world application examples with accompanying code. As a comprehensive guide to the emerging field of Embedded Artificial Intelligence, the book offers rich and in-depth content, a clear and logical structure, and a balanced approach to both theoretical analysis and practical applications. It provides significant reference value and can serve as an introductory and reference guide for researchers, scholars, students, engineers, and professionals interested in studying and implementing Embedded Artificial Intelligence. | |
Разместил: Ingvar16 6-09-2024, 20:26 | Комментарии: 0 | Подробнее
| | | |
|
| |
|
Навигация
Вход на сайт
Реклама
