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.NET MAUI for C# Developers

This book is targeted at anyone who has a fundamental understanding of C# and wishes to write cross-platform applications. If you are not a C# programmer but have experience with another objectoriented program, you should have no trouble following the examples.

OpenShift in Action

We think this is important, because the ultimate goal of DevOps is to enable and enhance communication between developer and operations teams that historically have been placed in adversarial (at best) relationships. To accomplish this, the two authors each specialize in one of these roles. For us, writing this book has been an amazing learning experience in how DevOps can work for just about anything, including writing a book.

Build Your Own Programming Language

This book is structured to guide the reader through the nuanced process of developing a programming language. Beginning with motivations and types of language implementations, Jeffery sets the stage for understanding the fundamental “why” behind language design. He intricately discusses organizing a bytecode language and differentiates between programming languages and libraries, laying a solid foundation for both novices and experienced programmers.

Low-Level Programming

This book is primarily for those of you beginning your journey in the vast arena of learning and understanding modern Linux kernel architecture and internals, Linux kernel module development and, to some extent, Linux device driver development. It’s also very much targeted at those of you who have already been working on Linux modules and/or drivers, who wish to gain a much deeper, well-structured understanding of Linux kernel architecture, memory management, task scheduling, cgroups, and synchronization.

Low-Code AI

This book was created as a first step for those who wish to become ML practitioners, not as a book to turn you into an ML expert. We do not cover the theory of ML in detail, nor do we cover all of the topics from statistics and mathematics needed to be a successful data scientist. We cover the theory that is needed for the projects discussed in this book as a way to ease you into working on ML projects, but going farther than that would be beyond the scope here. We do, however, give many references to resources where you can dive deeper if you are interested in doing so.

Machine Learning Crash Course for Engineers

This book is a crash course, delivering only what engineers need to know about machine learning to apply in their specialized domains. The book focuses more on applications and slightly less on the theory to cater to the needs of people in non-programming majors. The first three chapters are concept-building chapters, where the basics are discussed with relevant programming examples.

Machine Learning with Python Cookbook

This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.

Bayesian Optimization in Action

This book teaches Bayesian optimization, starting from its most basic compo-nents—Gaussian process regression and numerical optimization of an acquisition function—to the latest methods for handling large numbers of evaluations (aka obser-vations) and exotic design spaces. Along the way, it covers all the specializations you might need for a given project: constraint handling, multiple objectives, parallelized evaluation, and evaluation via pairwise comparison. You’ll find enough technical depth to make you comfortable with the tools and methods and enough real code to enable you to use those tools and methods for real work very quickly.

Painless Docker Unlock the Power of Docker and its Ecosystem

This guide doesn’t cover every aspect of Docker, but it does present the most important parts to learn and even master Docker and its rapidly expanding ecosystem. If you come across unfamiliar words or concepts, take your time and conduct your own online research.

Анорганик химиядан амалий ишлар

Қўлланмада лаборатория ишлари, анорганик моддалар классификацияси ва баъзи анорганик моддаларни синтез қилиш тартиби берилган.

Noorganik kimyodan laboratoriya mashg‘ulotlari

Quyidagi o`quv qo`llanmada talabalarning har bir laboratoriya ishiga tayyorlanishi uchun tushuntirish berilgan.

Java 17 Quick Syntax Reference

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

Quantum Computing in Action

This book is written for developers who are interested in knowing whether and how they can benefit from quantum computing, now or in the future, or in general, what impact will quantum computing have on their work. The reader is not expected to know anything about quantum physics. The book explains the areas where quantum computing might lead to improvements and how developers can use it similarly to how they use modern hardware (such as GPUs) without knowing the internal details.

Julia for Data Analysis

The book is divided into two parts. The first part introduces the basic concepts of the Julia language, introducing the type system, multiple dispatch, data structures, etc. The second part then builds on these concepts and presents data analysis—reading data, selecting, creating a DataFrame, split-apply-combine, sorting, joining, and reshaping— and finally finishes with a complete application. There is also a discussion of the Arrow data exchange format that allows Julia programs to co-exist with data analysis tools in R, Python, and Spark, to mention a few. The code patterns in all the chapters teach the reader good practices that result in high-performance data analysis.

Kotlin in Action

This book is written for all roles that interact with Kafka Connect environments. We have chosen to use the terms data engineers, site reliability engineers, and developers to distinguish between roles. Data engineers design and build pipelines to process and analyze data. This includes selecting the correct tools, designing the data flow, and testing the pipeline. Site reliability engineers are responsible for deploying and administering Kafka Connect environments.

Kubernetes: Up and Running Dive into the Future of Infrastructure

This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a signifi‐ cant amount of example code from this book into your product’s documentation does require permission.