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Название: Machine Learning Hybridization and Optimization for Intelligent Applications
Автор: Tanvir Habib Sardar, Bishwajeet Kumar Pandey
Издательство: CRC Press
Серия: Computational Intelligence Techniques
Год: 2025
Страниц: 367
Язык: английский
Формат: pdf (true), epub
Размер: 33.6 MB

This book discusses state-of-the-art reviews of the existing Machine Learning techniques and algorithms including hybridizations and optimizations. It covers applications of Machine Learning via Artificial Intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation. In recent years, the fusion of Machine Learning methodologies with optimization techniques has significantly advanced the realm of intelligent applications across diverse domains. This book delves into the symbiotic relationship between Machine Learning hybridization and optimization, presenting a comprehensive exploration of their integration to empower intelligent systems. The amalgamation of Machine Learning and optimization has revolutionized the way we approach problem-solving, enabling us to create sophisticated applications that adapt, evolve, and enhance their performance over time. This synergy has paved the way for innovation in various fields, from healthcare and finance to manufacturing and beyond. This book is aimed at graduate students and researchers in Machine Learning, Artificial Intelligence, and electrical engineering.

Название: System Reliability and Security: Techniques and Methodologies
Автор: Javaid Iqbal, Faheem Syeed Masoodi, Ishfaq Ahmad Malik, Shozab Khurshid, Iqra Saraf
Издательство: CRC Press
Год: 2024
Страниц: 273
Язык: английский
Формат: pdf (true), epub (true)
Размер: 29.6 MB

Because of the growing reliance on software, concerns are growing as to how reliable a system is before it is commissioned for use, how high the level of reliability is in the system, and how many vulnerabilities exist in the system before its operationalization. Equally pressing issues include how to secure the system from internal and external security threats that may exist in the face of resident vulnerabilities. These two problems are considered increasingly important because they necessitate the development of tools and techniques capable of analyzing dependability and security aspects of a system. These concerns become more pronounced in the cases of safety-critical and mission-critical systems. System Reliability and Security: Techniques and Methodologies focuses on the use of soft computing techniques and analytical techniques in the modeling and analysis of dependable and secure systems. Software Testing and Quality Assurance: In general, testing is intended to uncover the errors that creep in unintentionally during the design and construction of software systems/models or to verify whether the model meets the expected objectives. In contrast to traditional software development, AI/ML models are constructed inductively. That is, the behavior of the model is generated/designed from training data. Therefore, besides noisy data, the potential causes for undesired behavior in AI/ML include execution environment, design mis-specification, poor choice of model, program code, numerical instability, and non-convex objectives.

Название: MLOps with Ray: Best Practices and Strategies for Adopting Machine Learning Operations
Автор: Hien Luu, Max Pumperla, Zhe Zhang
Издательство: Apress
Год: 2024
Страниц: 342
Язык: английский
Формат: pdf (true), epub (true)
Размер: 11.8 MB

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing Machine Learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate Machine Learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale Machine Learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. Once data scientists have access to the needed dataset or available features, they will start analyzing them and evaluating whether they are suitable for the ML task at hand. To perform medium- to large-scale data analysis, they will need access to compute resources beyond their laptop so those data crunching needs will be completed in a short amount of time. This is where distributed data computation engines come into the picture. Examples of these engines are Apache Spark, Dask, and Ray. Ray is a compute framework to enable efficient distributed execution of Python and AI workloads, boasting a simple programming model and automatic parallelization. For Machine learning practitioners, data scientists, and software engineers who are focusing on building Machine Learning systems and infrastructure to bring ML models to production.

Название: Elegant Python: Simplifying Complex Solutions
Автор: Michael Huang
Издательство: Independently published
Год: 2024
Страниц: 132
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Python, often regarded as the Swiss Army knife of programming languages, uniquely blends simplicity and power. "Elegant Python: Simplifying Complex Solutions" is your comprehensive guide to harnessing this dynamic language's full potential. Unlock the art of writing code that solves problems and does so with grace and sophistication. This book takes you on a captivating journey through the realm of Python programming, where you'll master the skills required to craft elegant solutions to complex challenges. Explore the principles of Pythonic coding, emphasizing clarity, efficiency, and maintainability. From data manipulation and web development to machine learning and automation, "Elegant Python" covers a broad spectrum of topics, all with a central focus on creating code that stands out for its elegance and ingenuity. Whether you're a seasoned developer looking to refine your Python skills or a newcomer eager to learn the best practices from the start, this book has you covered. You'll gain a deep understanding of Python's capabilities and how to leverage them to create code that works and impresses with its simplicity and beauty.

Название: Learn coding with Python and jаvascript: 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 jаvascript, 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.

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.

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).

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.

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.

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.