Название: Shallow Learning vs. Deep Learning: A Practical Guide for Machine Learning Solutions
Автор: Ömer Faruk Ertuğrul, Josep M Guerrero, Musa Yilmaz
Издательство: Springer
Серия: The Springer Series in Applied Machine Learning
Год: 2024
Страниц: 283
Язык: английский
Формат: pdf (true), epub
Размер: 45.5 MB

This book explores the ongoing debate between shallow and Deep Learning in the field of Machine Learning. It provides a comprehensive survey of Machine Learning methods, from shallow learning to Deep Learning, and examines their applications across various domains. Shallow Learning vs Deep Learning: A Practical Guide for Machine Learning Solutions emphasizes that the choice of a Machine Learning approach should be informed by the specific characteristics of the dataset, the operational environment, and the unique requirements of each application, rather than being influenced by prevailing trends. In each chapter, the book delves into different application areas, such as engineering, real-world scenarios, social applications, image processing, biomedical applications, anomaly detection, natural language processing (NLP), speech recognition, recommendation systems, autonomous systems, and smart grid applications. By comparing and contrasting the effectiveness of shallow and Deep Learning in these areas, the book provides a framework for thoughtful selection and application of Machine Learning strategies. This guide is designed for researchers, practitioners, and students who seek to deepen their understanding of when and how to apply different Machine Learning techniques effectively. Through comparative studies and detailed analyses, readers will gain valuable insights to make informed decisions in their respective fields.

Название: Computational Thinking: First Algorithms, Then Code 2nd Edition
Автор: Paolo Ferragina, Fabrizio Luccio
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2024
Страниц: 204
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

This book offers a gentle motivation and introduction to computational thinking, in particular to algorithms and how they can be coded to solve significant, topical problems from domains such as finance, cryptography, Web search, and data compression. The book is suitable for undergraduate students in Computer Science, engineering, and applied mathematics, university students in other fields, high-school students with an interest in STEM subjects, and professionals who want an insight into algorithmic solutions and the related mindset. While the authors assume only basic mathematical knowledge, they uphold the scientific rigor that is indispensable for transforming general ideas into executable algorithms. A supporting website contains examples and Python code for implementing the algorithms in the book.

Название: Learning React: Modern Patterns for Developing React Apps, 3rd Edition (Early Release)
Автор: Alex Banks, Eve Porcello
Издательство: O’Reilly Media, Inc.
Год: 2024-10-14
Страниц: 111
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

If you want to learn how to build efficient React applications, this is your book. Ideal for web developers and software engineers who understand how jаvascript, CSS, and HTML work in the browser, this updated third edition provides best practices and patterns for writing modern React code. No prior knowledge of React or functional jаvascript is necessary. Using their learning road map, authors Alex Banks and Eve Porcello show you how to create UIs that can deftly display changes without page reloads on large-scale, data-driven websites. You'll also discover how to work with functional programming and the latest ECMAScript features. Once you learn how to build React components with this hands-on guide, you'll understand just how useful React can be in your organization.

Название: Introducing Python: Modern Computing in Simple Packages, 3rd Edition (Early Release)
Автор: Bill Lubanovic
Издательство: O’Reilly Media, Inc.
Год: 2024-10-14
Страниц: 106
Язык: английский
Формат: pdf, epub
Размер: 11.9 MB

Stuck in a coding conundrum? Whether you're an advanced beginner, an intermediate developer, or a curious newcomer, the complexities of coding can often feel like a labyrinth with no exit. With Python, however, you can start writing real code quickly—but where should you start? In this updated third edition, Bill Lubanovic acts as your personal guide to Python, offering a clear path through the intricacies and capabilities of this much-beloved coding language, including new chapters on AI models and performance enhancements. Easy to understand and enjoyable to read, this book not only teaches you the core concepts but also dives into practical applications that bridge the gap between learning and doing. Computing languages are easier to learn than human languages — they’re more concise and precise. Python is recognized as one of the easiest computing languages to learn, read, and write. It consists of data (like nouns in spoken languages) and instructions or code (like verbs). In alternating chapters, you’ll be introduced to Python’s basic code and data structures, learn how to combine them, and build up to more advanced ones. The programs that you read and write will get longer and more complex. You’ll learn the language, and what to do with it. We’ll begin with the core Python language and its “batteries included” standard library, and advance to finding, downloading, installing, and using some good third-party packages. My emphasis is on whatever I’ve actually found useful in twenty years of production Python development, rather than fringe topics or complex hacks.

Название: Learn AI-Assisted Python Programming, Second Edition
Автор: Leo Porter, Daniel Zingaro
Издательство: Manning Publications
Год: 2024
Страниц: 336
Язык: английский
Формат: epub
Размер: 10.1 MB

Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way—with the help of AI. It’s perfect for beginners, or anyone who’s struggled with the steep learning curve of traditional programming. AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it teaches you everything you need to start programming Python in an AI-first world. You’ll learn skills you can use to create working apps for data analysis, automating tedious tasks, and even video games. Plus, in this new edition, you’ll find groundbreaking techniques for breaking down big software projects into smaller tasks AI can easily achieve. The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. This book shows you how to create and improve Python programs using AI—even if you’ve never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you’ll even learn enough of the Python language to understand and improve what your AI assistant creates.

Название: The Quick Python Book, 4th Edition (MEAP v5)
Автор: Naomi Ceder
Издательство: Manning Publications
Год: 2024
Страниц: 415
Язык: английский
Формат: pdf (true)
Размер: 16.7 MB

A fast-paced introduction to Python for intermediate developers–now with coverage of generative AI! This revised Fourth Edition covers Python’s latest features, control structures, and libraries, plus new coverage of working with AI-generated Python code. Whether you’re new to Python or looking to advance your basic skills, The Quick Python Book, Fourth Edition will get you writing effective Python code fast. It concisely covers programming basics, while introducing Python's comprehensive standard library and unique features in depth and detail. You'll also learn to make the best use of AI coding tools like Copilot and Google's Colaboratory (Colab), comparing and contrasting human and AI code, and developing a mindset that can make the most of AI. This book is intended to help people get a solid general understanding of Python as quickly as possible, avoiding getting bogged down in advanced topics, but covering the essentials to write and read Python code. For readers familiar with the basics of programming who are interested in learning Python. To get the most benefit from this book, you’ll want to have some established skills in programming, either in Python or in another programming language like Java, C++, Ruby, jаvascript, or something similar. Since this book assumes an understanding of common data types and flow control structures, experience with only HTML or SQL may not be a good fit.

Название: Artificial Intelligence-Based System Models in Healthcare
Автор: A. Jose Anand, K. Kalaiselvi, Jyotir Moy Chatterje
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 500
Язык: английский
Формат: pdf (true)
Размер: 30.4 MB

Artificial Intelligence-Based System Models in Healthcare provide a comprehensive and insightful guide to the transformative applications of AI in the healthcare system. As technological advancements reshape medical practices, this handbook serves as a roadmap for researchers, healthcare professionals, and technology enthusiasts in the evolving healthcare delivery of AI-based systems. The contributor's and editors' knowledge provides a valuable resource to help understand how AI-based solutions will improve patient care and inspire the advancements of cutting-edge solutions. This book is edited into three sections, focusing on healthcare systems and AI-based system models. The first section explores the introduction to healthcare systems, focusing on the fundamental role of technology in reshaping the healthcare landscape. This section offers a unique perspective, emphasizing the integration of technology into healthcare ecosystems. The second part takes a deep dive into specific AI-based system model applications. From valuable insights into how AI systems offer valuable insights into the potential impact of patient outcomes, it also delves into topics of ML, image analysis, and biomedical text processing. The final section covers the future landscape of AI applications in healthcare practices. This section concludes by exploring the frontiers of AI-driven healthcare innovations and how they will aid the future of healthcare. This part provides details on how the ideas presented in the preceding section were put into practice in the form of a prototype. We used Python 3.7 for the prototype. The language was selected because of its popularity among scientists and its extensive library of mathematical and computational tools. SciKit-learn, Pandas, NumPy, SpaCy, Statsmodels, and Simpy were all used in the implementation.

Название: Combating Cyberattacks Targeting the AI Ecosystem: Assessing Threats, Risks, and Vulnerabilities
Автор: Aditya K. Sood
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 257
Язык: английский
Формат: epub (true)
Размер: 12.7 MB

This book explores in detail the AI-driven cyber threat landscape, including inherent AI threats and risks that exist in Large Language Models (LLMs), Generative AI applications, and the AI infrastructure. The book highlights hands-on technical approaches to detect security flaws in AI systems and applications utilizing the intelligence gathered from real-world case studies. Lastly, the book presents a very detailed discussion of the defense mechanisms and practical solutions to secure LLMs, GenAI applications, and the AI infrastructure. The chapters are structured with a granular framework, starting with AI concepts, followed by practical assessment techniques based on real-world intelligence, and concluding with required security defenses. Artificial Intelligence (AI) and cybersecurity are deeply intertwined and increasingly essential to modern digital defense strategies. The book is a comprehensive resource for IT professionals, business leaders, and cybersecurity experts for understanding and defending against AI-driven cyberattacks. Artificial Intelligence (AI) and cybersecurity are deeply intertwined and increasingly essential to modern digital defense strategies. Organizations are adopting AI technology exponentially, resulting in a significant evolution of the cyber threat landscape. Adversaries are leveraging AI capabilities to enhance their tactics and techniques to launch scalable cyberattacks in an automated manner. Prerequisites: Basic knowledge of programming languages commonly used in AI and cybersecurity, such as Python, Java, and C++. Experience with scripting languages like Bash or PowerShell for automating tasks. Familiarity with AI development tools such as TensorFlow, PyTorch, Keras, and Scikit-learn.

Название: Applying Artificial Intelligence In Project Management
Автор: Paul Boudreau
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 233
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

This book describes the Artificial Intelligence (AI) tools in concept and how they apply directly to project success. It also demonstrates the strategy and methods used to purchase and implement AI tools for project management. You will understand the difference between automating a task and changing it by using AI. Discover how AI uses data and the importance of data maintenance. Learn why projects fail and how using Artificial Intelligence for project management improves project success rates. The book features project management success stories and demonstrates how to leave behind that low project success rate for one that is 95 percent or higher. Supplemental teaching materials are available for use as a textbook. Prediction software consists of three components: the input data, software containing the Machine Learning algorithm, and output. The input data typically consists of several characteristics based on historical projects completed by an organization or within an industry. Each project is labeled as “successful” or “not successful.” Success can be user-defined for an organization. The input data was stored in a spreadsheet, but it could have also been stored in a database. The software program was written in Python. The software was hosted in the cloud and used Heroku as an interface for a Web browser. Creating the code for a neural network was fairly straightforward with modern programming utilities.

Название: Rexx Programmer's Reference, 2nd Edition
Автор: Howard Fosdick
Издательство: Rexx Language Association
Год: 2025
Страниц: 784
Язык: английский
Формат: pdf (true)
Размер: 18.0 MB

This book provides a comprehensive reference and programming guide to the Rexx programming language. It shows how to use the most popular implementations of Rexx and Rexx external function packages and is suited to both the programmer learning Rexx for the first time as well as the seasoned Rexx developer requiring a single, comprehensive reference manual. Originally developed for mainframes but highly portable across platforms-from servers to desktops to handhelds-Rexx is an easy yet powerful scripting language that's widely used for rapid application development. Rexx is a very underrated programming language; elegant in design, simple syntax, easy to learn, use and maintain, yet as powerful as any other scripting language available today. Rexx is as easy as jаvascript or PHP, yet about as powerful as Java or Perl. Rexx is unique in that it combines power with ease of use. Long the dominant scripting language on mainframes, it is definitely a “power” language, yet it is also so easy to use that its popularity has expanded to every conceivable platform. Today Rexx developers use the language on Windows, Linux, Unix, BSD, Macs, mainframes and many dozens of other systems ... and, there are many free and open source Rexx interpreters available.