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.

Название: C++ Brain Teasers: Exercise Your Mind
Автор: Anders Schau Knatten
Издательство: Pragmatic Bookshelf
Год: June 2024 (P1.0)
Страниц: 140
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

C++ is famous for getting all the default behaviors wrong and for sometimes making demons fly out of your nose. Through 25 puzzles, from the useful to the outright weird, we explore some of C++'s most interesting quirks. How does initialization actually work? Do temporaries even exist? As you work through each puzzle, you will peel off some of the layers of complexity of C++, getting a fundamental understanding of how the language works. This will help you write better code and recognize issues more easily while debugging. Each puzzle in the book is a complete, seemingly simple C++ program, but can you figure out the output for each, or will the answers stump you? Most of the programs compile and have deterministic, though sometimes surprising, output. Some might, however, have undefined behavior, where anything can happen, including making demons fly out of your nose! Yet others might have unspecified behavior with output that is not completely deterministic; we just know nothing as bad as nasal demons will happen. After working through the book, you'll have tools and techniques to help you write better and safer code, and a better understanding of the fundamentals of the language, the background for some of C++'s apparent weirdness, and a better feel for identifying bugs and unsafe code in your own programs. The book assumes basic knowledge of C++. If you want to run the programs yourself, you can use either a local C++ compiler or the online compilers.

Название: Machine Learning in Elixir: Learning to Learn with Nx and Axon
Автор: Sean Moriarity
Издательство: Pragmatic Bookshelf
Год: September 2024 (P1.0)
Страниц: 374
Язык: английский
Формат: epub
Размер: 10.1 MB

Stable Diffusion, ChatGPT, Whisper - these are just a few examples of incredible applications powered by developments in Machine Learning. Despite the ubiquity of Machine Learning applications running in production, there are only a few viable language choices for data science and machine learning tasks. Elixir's Nx project seeks to change that. With Nx, you can leverage the power of machine learning in your applications, using the battle-tested Erlang VM in a pragmatic language like Elixir. In this book, you'll learn how to leverage Elixir and the Nx ecosystem to solve real-world problems in computer vision, natural language processing, and more. The Elixir Nx project aims to make Machine Learningpossible without the need to leave Elixir for solutions in other languages. And even if concepts like linear models and logistic regression are new to you, you'll be using them and much more to solve real-world problems in no time. Start with the basics of the Nx programming paradigm - how it differs from the Elixir programming style you're used to and how it enables you to write Machine Learning algorithms. Use your understanding of this paradigm to implement foundational Machine Learning algorithms from scratch. Go deeper and discover the power of Deep Learning with Axon. Unlock the power of Elixir and learn how to build and deploy Machine Learning models and pipelines anywhere. Learn how to analyze, visualize, and explain your data and models.

Название: Deep Learning with JAX (Final Release)
Автор: Grigory Sapunov
Издательство: Manning Publications
Год: 2024
Страниц: 410
Язык: английский
Формат: pdf (true)
Размер: 39.8 MB

Accelerate Deep Learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of Deep Learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. Deep Learning with JAX is a hands-on guide to using JAX for Deep Learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. The JAX Python mathematics library is used by many successful Deep Learning organizations, including Google’s groundbreaking DeepMind team. This exciting newcomer already boasts an amazing ecosystem of tools including high-level Deep Learning libraries Flax by Google, Haiku by DeepMind, gradient processing and optimization libraries, libraries for evolutionary computations, Federated Learning, and much more! JAX brings a functional programming mindset to Python Deep Learning, letting you improve your composability and parallelization in a cluster. For intermediate Python programmers who are familiar with Deep Learning.

Название: Generative AI in Action (Final Release)
Автор: Amit Bahree
Издательство: Manning Publications
Год: 2024
Страниц: 466
Язык: английский
Формат: pdf (true)
Размер: 29.3 MB

Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! GenAI has created new opportunities for organizations of all sizes. You can easily use tools like ChatGPT, Bard, and Stable Diffusion to generate text and images for product catalogs, marketing campaigns, technical reporting, and other common tasks. Coding assistants like Copilot are accelerating productivity in software teams. In this insightful book, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. The book teaches you how to create and use generative models for tasks and use cases. It focuses on this technology’s practical and hands-on aspects and how it works. It does not dive deep into the science, but it references the papers and scientific breakthroughs that have helped develop some of the technology—you can see these at the end of the book. This book is designed to provide a comprehensive understanding of generative AI and its potential within an enterprise context. It explores foundational models, large language models, and related algorithms and architectures, offering readers a thorough grasp of these advanced technologies. Practical insights and examples are provided to help develop and deploy generative AI models, ensuring that readers can apply these concepts in real-world scenarios. Advanced topics such as prompt engineering, retrieval-augmented generation, and model adaptation are discussed in detail, giving readers an in-depth understanding of these cutting-edge techniques. For enterprise architects and senior developers interested in upgrading their architectures with generative AI.