Abhishek Mishra Knihy

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.

Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. • Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building • Discover common neural network frameworks with Amazon SageMaker • Solve computer vision problems with Amazon Rekognition • Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem

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.

Jump into the app development world with confidence! iOS Swift 24-Hour Trainer combines book and video lessons in Apple's Swift programming language to prepare you to build iPhone and iPad apps and distribute them through the Appstore.

A practical, real-world introduction to AWS tools and concepts Amazon Web Services for Mobile Developers: Building Apps with AWS presents a professional view of cloud computing and AWS for experienced iOS/Android developers and technical/solution architects.

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