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Summary: Create cryptocurrency and blockchain applications by examining the key algorithms and concepts pertaining to blockchains, transaction processing, mining, distributed consensus, and anonymous currencies. In this book, youll develop a fully functional cryptocurrency from scratch in the Python language. Practical Blockchains and Cryptocurrencies is a reference for development of blockchain applications and provides you with rigorous information on cryptography and the theory underlying blockchains.

Summary: Modern marketing professionals looking to adopt influencer marketing for their brands face equally modern challenges. Like finding the right talent, tracking and measuring results and quantifying how this new marketing opportunity aligns with the overall strategy. Influencer Marketing for Brands is the field guide for the digital age. After working with hundreds of brands from across the globe, author Aron Levin shares his insider knowledge gained from research, strategy, and hands-on experience from more than 10,000 successful collaborations with influencers on Instagram and YouTube. He provides you with valuable insights that help you eliminate guesswork and avoid common mistakes.

Summary: Learn how to make better decisions and write cleaner Ruby code. This book shows you how to avoid messy code that is hard to test and which cripples productivity. Author Carleton DiLeo shares hard-learned lessons gained from years of experience across numerous codebases both large and small. Each chapter covers the topics you need to know to make better decisions and optimize your productivity. Many books will tell you how to do something; this book will tell you why you should do it. Start writing code you love. What You Will Learn Build better classes to help promote code reuse Improve your decision making and make better, smarter choices Identify bad code and fixed it Create quality names for all of your variables, classes, and modules Write better, concise classes Improve the quality of your methods Properly use modules Clarify your Boolean logic See when and how you refactor Improve your understanding of TDD and write better tests Who This Book Is For This book is written for Ruby developers. There is no need to learn a new language or translate concepts to Ruby.

Summary: Learn how to build a design system framed within the context of your specific business needs. This book guides you through the process of defining a design language that can be understood across teams, while also establishing communication strategies for how to sell your system to key stakeholders and other contributors. With a defined set of components and guidelines, designers can focus their efforts on solving user needs rather than recreating elements and reinventing solutions. You'll learn how to use an interface inventory to surface inconsistencies and inefficient solutions, as well as how to establish a component library by documenting existing patterns and creating new ones. You'll also see how the creation of self-documenting styles and components will streamline your UX process. Building Design Systems provides critical insights into how to set up a design system within your organization, measure the effectiveness of that system, and maintain it over time. You will develop the skills needed to approach your design process systematically, ensuring that your design system achieves the purpose of your organization, your product, and your team. What You'll Learn Develop communication strategies necessary to gain buy-in from key stakeholders and other teams Establish principles based on your specific needs Design, build, implement, and maintain a design system from the ground up Measure the effectiveness of your system over time Who This Book Is For All teams, large and small, seeking to unify their design language through a cohesive design system and create buy-in for design thinking within their organization; UX, visual, and interaction designers, as well as product managers and front-end developers will benefit from a systematic approach to design.

Summary: Explore the world of using machine learning methods with deep computer vision, sensors and data in sports, health and fitness and other industries. Accompanied by practical step-by-step Python code samples and Jupyter notebooks, this comprehensive guide acts as a reference for a data scientist, machine learning practitioner or anyone interested in AI applications. These ML models and methods can be used to create solutions for AI enhanced coaching, judging, athletic performance improvement, movement analysis, simulations, in motion capture, gaming, cinema production and more. Packed with fun, practical applications for sports, machine learning models used in the book include supervised, unsupervised and cutting-edge reinforcement learning methods and models with popular tools like PyTorch, Tensorflow, Keras, OpenAI Gym and OpenCV. Author Kevin Ashley--who happens to be both a machine learning expert and a professional ski instructor--has written an insightful book that takes you on a journey of modern sport science and AI. Filled with thorough, engaging illustrations and dozens of real-life examples, this book is your next step to understanding the implementation of AI within the sports world and beyond. Whether you are a data scientist, a coach, an athlete, or simply a personal fitness enthusiast excited about connecting your findings with AI methods, the authors practical expertise in both tech and sports is an undeniable asset for your learning process. Todays data scientists are the future of athletics, and Applied Machine Learning for Health and Fitness hands you the knowledge you need to stay relevant in this rapidly growing space. You will: Use multiple data science tools and frameworks Apply deep computer vision and other machine learning methods for classification, semantic segmentation, and action recognition Build and train neural networks, reinforcement learning models and more Analyze multiple sporting activities with deep learning Use datasets available today for model training Use machine learning in the cloud to train and deploy models Apply best practices in machine learning and data science.

Summary: Evolve your traditional intranet platform into a next-generation digital workspace with this comprehensive book. Through in-depth coverage of strategies, methods, and case studies, you will learn how to design and build an employee experience platform (EXP) for improved employee productivity, engagement, and collaboration. In Build a Next-Generation Digital Workplace, author Dr. Shailesh Kumar Shivakumar takes you through the advantages of EXPs and shows you how to successfully implement one in your organization. This book provides extensive coverage of topics such as EXP design, user experience, content strategy, integration, EXP development, collaboration, and EXP governance. Real-world case studies are also presented to explore practical applications. Employee experience platforms play a vital role in engaging, empowering, and retaining the employees of an organization. Next-generation workplaces demand constant innovation and responsiveness, and this book readies you to fulfill that need with an employee experience platform. You will: Understand key design elements of EXP, including the visual design, EXP strategy, EXP transformation themes, information architecture, and navigation design. Gain insights into end-to-end EXP topics needed to successfully design, implement, and maintain next-generation digital workplace platforms. Study methods used in the EXP lifecycle, such as requirements and design, development, governance, and maintenance Execute the main steps involved in digital transformation of legacy intranet platforms to EXP. Discover emerging trends in digital workplace such as gamification, machine-led operations model and maintenance model, employee-centric design (including persona based design and employee journey mapping), cloud transformation, and design transformation. Comprehend proven methods for legacy Intranet modernization, collaboration, solution validation, migration, and more.

Summary: Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways. You will: Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples.