Search results for "python for generative ai"
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Generative AI in the English Composition Classroom
Designed to cater to the needs of both novice and seasoned writing instructors, this book provides a range of practical and adaptable strategies for integrating generative AI into English writing curricula.Generative AI in the English Composition Classroom proposes strategic methodologies to ensure that AI is utilized as a facilitator of learning and creativity, rather than as a shortcut to academic success. With a particular emphasis on sophisticated large language models such as Claude, ChatGPT, and Gemini, this book critically addresses potential challenges, including concerns related to academic integrity. It includes case studies and practical strategies to exemplify how AI can enhance the writing process while emphasizing the continuing importance of a solid foundation in writing structure, processes, and rhetorical strategies. These case studies and strategies are designed for immediate application, offering educators and students practical tools to effectively navigate AI-augmented writing environments. Finally, the book looks to the future, discussing the evolving skillsets required in the workforce and how educators can equip students for a future in which AI is an integral component.A forward-thinking and invaluable guide, this book will be of interest to educators involved in teaching English composition and writing.
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Python for Engineers and Scientists
The text focuses on the basics of Python programming fundamentals and introduction to present-day applications in technology and the upcoming state-of-art trends in a comprehensive manner. The text is based on Python 3.x and it covers the fundamentals of Python with object-oriented concepts having numerous worked-out examples. It provides a learning tool for the students of beginner level as well as for researchers of advanced level. Each chapter contains additional examples that explain the usage of methods/functions discussed in the chapter. It provides numerous programming examples along with their outputs.The book:Includes programming tips to highlight the important concepts and help readers avoid common programming errorsProvides programming examples along with their outputs to ensure the correctness and help readers in mastering the art of writing efficient Python programsContains MCQs with their answers; conceptual questions and programming questions; and solutions to some selected programming questions, for every chapterDiscusses applications like time zone converter and password generators at the endCovers fundamental of Python up to object oriented concepts including regular expressionThe book offers a simple and lucid treatment of concepts supported with illustrations for easy understanding, provides numerous programming examples along with their outputs, and includes programming tips to highlight the important concepts. It will be a valuable resource for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, electronics and communication engineering, and computer engineering.
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Python Programming for Biology
Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.
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Kivy: Interactive Applications in Python
into the world of Kivy.This book is aimed at Python developers who are familiar with Python and have a good understanding of concepts like inheritance, classes, and instances. No previous experience of Kivy is required, though some knowledge of event handling, scheduling, and user interfaces, in general, would boost your learning.
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Generative Ai In Clinical Practice: A Physician's Guide To Transforming Medicine
Generative AI in Clinical Practice: A Physician's Guide to Transforming Medicine, is a practical guide for clinicians who want to understand and apply generative AI in everyday care. Designed for readers without a background in computer science, the book explains how large language models and related tools are being integrated into clinical workflows, with clarity, realism, and clinical relevance.You'll explore how generative AI is already supporting tasks like ambient documentation, diagnostic reasoning, patient education, and care coordination. Each chapter combines clinical vignettes, implementation strategies, and curated examples from frontline practice to show what works, and what to watch out for.The book also addresses critical issues of ethics, bias, regulation, and physician oversight. Rather than offering abstract predictions, it provides grounded, actionable guidance for safely using AI at the point of care.Whether you are a physician, nurse, or healthcare executive, this book will help you make informed decisions about AI adoption. It will also give you the language and insight needed to lead conversations about these emerging tools within your organization. Clear-eyed and practical, this guide is written to help clinicians gain confidence, not just in the technology, but in how to use it wisely.
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Integration Strategies of Generative AI in Higher Education
Amidst the rapid evolution of educational technology, a pressing challenge confronts higher education institutions: how to effectively integrate generative artificial intelligence (AI) into their existing frameworks. As universities strive to adapt to the digital age, they are met with the complexities of incorporating AI-driven solutions to enhance teaching, learning, and administrative processes. However, the lack of comprehensive strategies and guidance hinders their ability to leverage AI's full potential, leaving educators and administrators grappling with uncertainty. In response to this critical dilemma, Integration Strategies of Generative AI in Higher Education emerges as a guide for clarity and innovation. By offering methodological insights and practical frameworks, this book equips higher education stakeholders with the tools needed to navigate the intricacies of AI integration. From curriculum enhancement to AI-driven content creation, the book provides actionable strategies tailored to the unique needs and challenges of higher education institutions. As educators, administrators, and policymakers seek to harness the transformative power of AI in education, this book serves as an indispensable roadmap for success. By synthesizing best practices from diverse educational contexts and addressing key considerations such as regulatory frameworks and ethical implications, this book empowers stakeholders to embrace AI with confidence, ushering in a new era of innovation and excellence in higher education.
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Mastering Python for Finance
Take your financial skills to the next level by mastering cutting-edge mathematical and statistical financial applicationsKey FeaturesExplore advanced financial models used by the industry and ways of solving them using PythonBuild state-of-the-art infrastructure for modeling, visualization, trading, and moreEmpower your financial applications by applying machine learning and deep learningBook DescriptionThe second edition of Mastering Python for Finance will guide you through carrying out complex financial calculations practiced in the industry of finance by using next-generation methodologies. You will master the Python ecosystem by leveraging publicly available tools to successfully perform research studies and modeling, and learn to manage risks with the help of advanced examples.You will start by setting up your Jupyter notebook to implement the tasks throughout the book. You will learn to make efficient and powerful data-driven financial decisions using popular libraries such as TensorFlow, Keras, Numpy, SciPy, and sklearn. You will also learn how to build financial applications by mastering concepts such as stocks, options, interest rates and their derivatives, and risk analytics using computational methods. With these foundations, you will learn to apply statistical analysis to time series data, and understand how time series data is useful for implementing an event-driven backtesting system and for working with high-frequency data in building an algorithmic trading platform. Finally, you will explore machine learning and deep learning techniques that are applied in finance.By the end of this book, you will be able to apply Python to different paradigms in the financial industry and perform efficient data analysis.What you will learnSolve linear and nonlinear models representing various financial problemsPerform principal component analysis on the DOW index and its componentsAnalyze, predict, and forecast stationary and non-stationary time series processesCreate an event-driven backtesting tool and measure your strategiesBuild a high-frequency algorithmic trading platform with PythonReplicate the CBOT VIX index with SPX options for studying VIX-based strategiesPerform regression-based and classification-based machine learning tasks for predictionUse TensorFlow and Keras in deep learning neural network architectureWho this book is forIf you are a financial or data analyst or a software developer in the financial industry who is interested in using advanced Python techniques for quantitative methods in finance, this is the book you need! You will also find this book useful if you want to extend the functionalities of your existing financial applications by using smart machine learning techniques. Prior experience in Python is required.
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Interactive Data Visualization with Python
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of PythonKey FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook DescriptionWith so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is forThis book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.
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