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Machine learning in the AWS Cloud : add intellegence to applications with Amazon SageMaker and Amazon Rekognition
Recent advances in storage, CPU, and GPU technology, coupled with the ease with which you can create virtual computing resources in the cloud, and the availability of Python libraries such as Pandas, Matplotlib, TensorFlow, and Scikit-learn, have made it possible to build and deploy machine learning (ML) systems at scale and get results in real-time. \"Machine learning in the AWS Cloud\" offers an introduction to the machine learning capabilities of the Amazon Web Services ecosystem. The book is filled with illustrative examples that are designed to help with solutions to real-world regression and classification challenges. While prior experience with ML is not a requirement, some knowledge of Python and a basic knowledge of Amazon Web Services is a plus.
Book
Machine Learning for IOS Developers
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.
eBook
IOS code testing : test-driven development and behavior-driven development with Swift
Put into motion practical examples to master Test-Driven Development (TDD) and acceptance testing in Swift. This book uses a pragmatic approach to writing well-tested code and provides techniques that can be used to retrofit tests to legacy code bases. You'll be introduced to basic principles of TDD, such as Test First, Red-Green-Refactor, Remove Duplicate code, Dependency Injection, and Single Responsibility. Approaches covered include TDD, behavior-driven development (BDD), UI, and acceptance testing with common standard/open source frameworks. iOS Code Testing offers helpful instruction to teach iOS developers to retrospectively fit tests to legacy code, refactor legacy code so as to make the code more testable, install and configure a popular Swift BDD framework, practice BDD with Xcode, and create automated UI tests with Xcode. Additionally, many projects have legacy code bases. Legacy code is often seen as a blocker when it comes to implementing any kind of testing.
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