Название: AI Horizons: Shaping a Better Future Through Responsible Innovation and Human Collaboration
Автор: Enamul Haque
Издательство: Mercury Learning and Information
Год: 2024
Страниц: 245
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

As we stand on the brink of a new era, this book offers a comprehensive exploration of AI's ethical, social, and technological implications. It explores privacy, fairness, accountability, and the digital divide. The book features real-world case studies across diverse industries to illustrate AI's transformative power and its potential to revolutionize healthcare, education, transportation, and more. Special attention is given to the adoption of AI in emerging markets and emphasizes the importance of human-AI collaboration, where AI augments human intelligence and creativity rather than replacing it. It examines the need for human-centric design while also exploring AI governance and the role of multi-stakeholder collaboration in shaping the future of this technology. By fostering a comprehensive understanding of AI and its myriad implications, the book inspires thoughtful dialogue and responsible action to ensure a brighter, more equitable future for all. Machine Learning (ML) represents a specialized branch within the broader field of artificial intelligence, focusing primarily on crafting computer programs capable of learning and evolving through experience. At its heart, ML revolves around developing algorithms designed to sift through and analyze extensive datasets, discerning patterns within. This process empowers these systems to make informed predictions or decisions based on their analyses. Crucially, ML algorithms are broadly classified into three categories: supervised learning, unsupervised learning, and reinforcement learning. Each type represents a unique method for training these algorithms, each with its distinct set of uses and inherent limitations.

Название: Android Studio Koala Essentials - Kotlin Edition: Developing Android Apps Using Android Studio Koala Feature Drop and Kotlin
Автор: Neil Smyth
Издательство: Payload Media
Год: 2024
Страниц: 845
Язык: английский
Формат: epub
Размер: 45.0 MB

Unlock the Full Potential of Android Development. Are you ready to create powerful, modern Android apps using Kotlin? This comprehensive guide, fully updated for the Android Studio Koala Feature Drop (2024.1.2), is your ultimate companion to mastering Android app development from start to finish. Get Started with Confidence: From setting up your Android development and testing environment to mastering the fundamentals of Kotlin, including data types, control flow, functions, lambdas, and object-oriented programming, we’ll guide you every step of the way. Dive Deep into Advanced Techniques: Learn asynchronous programming with Kotlin coroutines and flow, and gain expertise in Android Architecture Components such as view models, lifecycle management, Room database access, app navigation, live data, and more. Master Essential Android Features: Develop skills in handling touch screens, gesture recognition, audio playback, and recording, and explore advanced functionalities like intents, printing, transitions, and foldable device support. Why This Book? Whether you’re a seasoned developer ready to elevate your Android skills or an experienced programmer eager to explore new horizons, this book offers everything you need to succeed. Packed with practical examples and expert insights, it’s designed to transform your app ideas into reality.

Название: Better APIs : Quality, Stability, Observability
Автор: Mikael Vesavuori
Издательство: Leanpub
Год: 2024-08-24
Страниц: 100
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Master the art of building high-quality, stable, and observable APIs with this hands-on guide—perfect for developers and architects looking to elevate their API game and ensure their projects are reliable, maintainable, and scalable. This book is a practical guide to improving the quality, stability, and observability of your APIs. Through a combination of theoretical insights and a hands-on example project, you'll learn how to create and maintain production-grade APIs that meet high standards of excellence. Better APIs: Quality, Stability, Observability is designed for developers and architects who want to deepen their understanding of API development and ensure their APIs are reliable, maintainable, and easy to monitor. APIs are the backbone of modern software architecture, and ensuring they are of high quality, stable, and observable is critical to the success of any software project. This book provides you with the tools and knowledge to create APIs that meet these criteria, helping you avoid common pitfalls and ensuring your APIs can scale and evolve over time. Whether you're looking to refine your existing APIs or build new ones from scratch, Better APIs: Quality, Stability, Observability will provide you with the insights and techniques you need to succeed and create APIs that are not only functional but also robust, maintainable, and easy to monitor.

Название: CompTIA Tech+ CertMike: Prepare. Practice. Pass the Test! Get Certified!: Exam FC0-U71, 2nd Edition
Автор: Mike Chapple
Издательство: Sybex
Год: 2025
Страниц: 384
Язык: английский
Формат: epub (true)
Размер: 57.6 MB

Prepare for, practice, and pass the CompTIA Tech+ certification exam on your first try. In the newly updated second edition of CompTIA Tech+ CertMike: Prepare. Practice. Pass the Test! Get Certified! for exam FC0-U71, veteran IT expert and IT educator, Mike Chapple, skips the fluff and dives straight into exactly what you need to ace the entry-level CompTIA Tech+ certification exam on your first try. Filled with the kind of no-nonsense, straight-to-business info you're looking for, the book includes coverage of every relevant exam domain, a full practice exam, and additional multiple choice practice questions with detailed answer explanations. You'll learn all about IT infrastructure, software development, database use, software installation, network connectivity, and more, as well as how all this valuable knowledge applies to common on-the-job scenarios. Prepare smarter and faster for the Tech+ certification exam with proven strategies created by the bestselling Mike Chapple and his team at CertMike. This book's perfect for people just getting interested in information technology and working towards a more advanced certification, like the CompTIA A+ or Security+ credentials. It's also a can't-miss resource for anyone who wants to learn basic computer literacy skills.

Название: Regression and Machine Learning for Education Sciences Using R
Автор: Cody Dingsen
Издательство: Routledge
Год: 2025
Страниц: 377
Язык: английский
Формат: pdf (true), epub
Размер: 54.0 MB

This book provides a conceptual introduction to regression analysis and Machine Learning and their applications in education research. It discusses their diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making. Offering an applications-oriented approach while mapping out fundamental methodological developments, this book lays a sound foundation for understanding essential regression and Machine Learning concepts for data analytics. The first part of the book discusses regression analysis and provides a sturdy foundation to understand the logic of Machine Learning. With each chapter, the discussion and development of each statistical concept and data analytical technique is presented from an applied perspective, with the statistical results providing insights into decisions and solutions to problems using R. Based on practical examples, and written in a concise and accessible style, the book is learner-centric and does a remarkable job in breaking down complex concepts. Specifically, we discuss the concepts and relationships related to Big Data, Data Science, data analytics, data mining, and Machine Learning.

Название: Effective C, 2nd Edition: An Introduction to Professional C Programming
Автор: Robert C. Seacord
Издательство: No Starch Press
Год: 2025
Страниц: 304
Язык: английский
Формат: True EPUB,MOBI
Размер: 10.1 MB

Effective C, 2nd edition, is an introduction to essential C language programming that will soon have you writing programs, solving problems, and building working systems. The latest release of the C programming language, C23, enhances the safety, security, and usability of the language. This second edition of Effective C has been thoroughly updated to cover C23, offering a modern introduction to C that will teach you best practices for writing professional, effective, and secure programs that solve real-world problems. C is used as a target language for compilers to build operating systems, to teach fundamentals of computing, and for embedded and general-purpose programming. There is a large amount of legacy code written in C. The C standards committee is extremely careful not to break existing code, providing a smooth pass for modernizing this code to take advantage of modern language features. C is often used in embedded systems because it is a small and efficient language. Embedded systems are small computers that are embedded in other devices, such as cars, appliances, and medical devices. Your favorite programming language and library are written in C (or were at one time). There are many libraries available for C. This makes it easy to find libraries that can be used to perform common tasks. Overall, C is a powerful and versatile language that is still widely used today. It is a good choice for programmers who need a fast, efficient, and portable language.

Название: Deep Generative Modeling, 2nd Edition
Автор: Jakub M. Tomczak
Издательство: Springer
Год: 2024
Страниц: 325
Язык: английский
Формат: pdf (true), epub
Размер: 50.2 MB

This first comprehensive book on models behind Generative AI has been thoroughly revised to cover all major classes of deep generative models: mixture models, Probabilistic Circuits, Autoregressive Models, Flow-based Models, Latent Variable Models, GANs, Hybrid Models, Score-based Generative Models, Energy-based Models, and Large Language Models. In addition, Generative AI Systems are discussed, demonstrating how deep generative models can be used for neural compression, among others. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics of Machine Learning, Deep Learning, and programming in Python and PyTorch (or other Deep Learning libraries). It should find interest among students and researchers from a variety of backgrounds, including computer science, engineering, Data Science, physics, and bioinformatics who wish to get familiar with deep generative modeling. In order to engage with a reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on the author's GitHub site.

Название: Deep Learning with PyTorch : Step-by-Step A Beginner's Guide
Автор: Daniel Voigt Godoy
Издательство: Leanpub
Год: 2024-07-29: v1.2
Страниц: 1047
Язык: английский
Формат: pdf (true)
Размер: 33.5 MB

If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it. This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time). PyTorch is the fastest-growing framework for developing Deep Learning models and it has a huge ecosystem. That is, there are many tools and libraries developed on top of PyTorch. It is the preferred framework in academia already and is making its way in the industry. This book aims to get you started with PyTorch while giving you a solid understanding of how it works. In this book, I will guide you through the development of many models in PyTorch, showing you why PyTorch makes it much easier and more intuitive to build models in Python: autograd, dynamic computation graph, model classes and much, much more. I wrote this book for beginners in general—not only PyTorch beginners. Every now and then, I will spend some time explaining some fundamental concepts that I believe are essential to have a proper understanding of what’s going on in the code.

Название: Automate Your Home Using Go: Build a Personal Data Center with Raspberry Pi, Docker, Prometheus, and Grafana
Автор: Ricardo Gerardi, Mike Riley
Издательство: Pragmatic Bookshelf
Год: August 2024 (v.P1.0)
Страниц: 275
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Take control of your home and your data with the power of the Go programming language. Build extraordinary and robust home automation solutions that rival much more expensive, closed commercial alternatives, using the same tools found in high-end enterprise computing environments. Best-selling Pragmatic Bookshelf authors Ricardo Gerardi and Mike Riley show how you can use inexpensive Raspberry Pi hardware and excellent, open source Go-based software tools like Prometheus and Grafana to create your own personal data center. Using the step-by-step examples in the book, build useful home automation projects that you can use as a blueprint for your own custom projects. With just a Raspberry Pi and the Go programming language, build your own personal data center that coordinates and manages your home automation, leveraging the same high-powered software used by large enterprises. The projects in this book are easy to assemble, no soldering or electrical engineering expertise required. Our objective for the book was to avoid as much electrical engineering and wiring as possible. You can complete each project in this book without ever picking up a soldering gun. While it’s commendable to use one for appropriate cases, this book focuses more on software than hardware. We also didn’t want to have hardware components fail as a result of poor soldering or confusing wiring diagrams, so we opted to make the hardware configuration for these projects as simple as possible to avoid any frustration or expensive mistakes. This book is for developers familiar with the Go programming language who want to do more with it than just the usual integration and microservices that Go is typically used for. It is also for home automation tinkerers and electronics hobbyists interested in learning how a language like Go can be more powerful and make software projects easier to build and maintain, especially when compared to other languages used in home automation like Perl and Python.

Название: Energy Efficiency and Robustness of Advanced Machine Learning Architectures: A Cross-Layer Approach
Автор: Alberto Marchisio, Muhammad Shafique
Издательство: CRC Press
Серия: Chapman & Hall/CRC Artificial Intelligence and Robotics Series
Год: 2025
Страниц: 361
Язык: английский
Формат: pdf (true), epub
Размер: 51.9 MB

Machine Learning (ML) algorithms have shown a high level of accuracy, and applications are widely used in many systems and platforms. However, developing efficient ML-based systems requires addressing three problems: energy-efficiency, robustness, and techniques that typically focus on optimizing for a single objective/have a limited set of goals. This book tackles these challenges by exploiting the unique features of advanced ML models and investigates cross-layer concepts and techniques to engage both hardware and software-level methods to build robust and energy-efficient architectures for these advanced ML networks. More specifically, this book improves the energy efficiency of complex models like CapsNets, through a specialized flow of hardware-level designs and software-level optimizations exploiting the application-driven knowledge of these systems and the error tolerance through approximations and quantization. This book also improves the robustness of ML models, in particular for SNNs executed on neuromorphic hardware, due to their inherent cost-effective features. This book integrates multiple optimization objectives into specialized frameworks for jointly optimizing the robustness and energy efficiency of these systems. This is an important resource for students and researchers of computer and electrical engineering who are interested in developing energy efficient and robust ML.