Abhishek Mishra Bücher

Mastering Test-Driven Development (TDD) and acceptance testing in Swift is achieved through practical examples that guide readers in implementing these methodologies effectively. The book focuses on hands-on techniques, enabling developers to enhance their skills and confidence in writing robust, testable code. With a clear emphasis on real-world applications, it serves as a valuable resource for both beginners and experienced programmers looking to improve their development practices.

Exploring themes of love and despair, the narrative follows a character's journey through profound encounters, including a meeting with a mendicant and a confrontation with death. The story unfolds with an emphasis on emotional depth and the complexities of human experience, skillfully avoiding excessive alliteration while capturing the essence of each moment.

The book showcases an innovative methodology, particularly through the use of gamified depth interviews, which enhances the research rigor at every stage, from literature review to the development of constructs. It includes the original contribution of a design perception scale, highlighting the author's insightful approach and thorough examination of the subject matter. Dr. Sharad Sarin commends the work as an excellent contribution to the field.

Focusing on Azure's capabilities, this book guides Java programmers through building and deploying applications on the Microsoft Azure cloud platform. It details various deployment models and offers practical examples for using Azure WebApp, Azure Kubernetes Service, Azure Functions, and Azure Spring Cloud. Additionally, it explores integration with essential components like Graph API, Azure Storage, Azure Redis Cache, and Azure SQL, providing a comprehensive resource for leveraging Azure features in Java development.

Focusing on special integrals and series sums, this book serves upper-undergraduate and graduate mathematics students. It covers differentiation and integration methods for summing series, including binomial and trigonometric series, and explores sums involving variables with fractional powers. Utilizing complex variables, it derives theorems leading to special integrals and series sums. Additionally, it discusses Bessel coefficients and functions, as well as pseudo-exponential functions. The content is structured into two parts, with problem-solving chapters complementing the theory. Prerequisites include calculus, complex analysis, and Fourier series knowledge.

The book focuses on design patterns essential for creating scalable AI solutions tailored to organizational needs. It emphasizes practical application, guiding readers through the development and deployment processes to foster innovation in intelligent automation. By leveraging these design patterns, readers can enhance their capabilities in AI project implementation, ensuring their solutions are effective and forward-thinking.

Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models'both pre-trained and user-built'with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: -Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics -Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming -Develop skills in data acquisition and modeling, classification, and regression.-Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) -Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn' & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps