Libros de Programación, página 36 de 305

User experience UX design practices have seen a fundamental shift as more and more software products incorporate machine learning ML components and artificial intelligence AI algorithms at their core This book will probe into UX design s role in making technologies inclusive and enabling user collaboration with AI AI ML based systems have changed the way of traditional UX design Instead of programming a method to do a specific action creators of these systems provide data and nurture them to curate outcomes based on inputs These systems are dynamic and while AI systems change over time their user experience in many cases does not adapt to this dynamic nature Applied UX Design for Artificial Intelligence will explore this problem addressing the challenges and opportunities in UX design for AI ML systems look at best practices for designers managers and product creators and showcase how individuals from a non technical background can collaborate effectively with AI and Machine learning teams You Will Learn Best practices in UX design when building human centric AI products or features Ability to spot opportunities for applying AI in their organizations Advantages and limitations of AI when building software products Ability to collaborate and commun

The expert guide to creating production machine learning solutions with ML NET ML NET brings the power of machine learning to all NET developers and Programming ML NET helps you apply it in real production solutions Modeled on Dino Esposito s best selling Programming ASP NET this book takes the same scenario based approach Microsoft s team used to build ML NET itself After a foundational overview of ML NET s libraries the authors illuminate mini frameworks ML Tasks for regression classification ranking anomaly detection and more For each ML Task they offer insights for overcoming common real world challenges Finally going far beyond shallow learning the authors thoroughly introduce ML NET neural networking They present a complete example application demonstrating advanced Microsoft Azure cognitive services and a handmade custom Keras network showing how to leverage popular Python tools within NET 14 time Microsoft MVP Dino Esposito and son Francesco Esposito show how to Build smarter machine learning solutions that are closer to your user s needs See how ML NET instantiates the classic ML pipeline and simplifies common scenarios such as sentiment analysis fraud detection and price prediction Implement data processing and training and produ

This book comprehensively covers the topic of recommender systems which provide personalized recommendations of products or services to users based on their previous searches or purchases Recommender system methods have been adapted to diverse applications including query log mining social networking news recommendations and computational advertising This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity The chapters of this book are organized into three categories Algorithms and evaluation These chapters discuss the fundamental algorithms in recommender systems including collaborative filtering methods content based methods knowledge based methods ensemble based methods and evaluation Recommendations in specific domains and contexts the context of a recommendation can be viewed as important side information that affects the recommendation goals Different types of context such as temporal data spatial data social data tagging data and trustworthiness are explored Advanced topics and applications Various robustness aspects of recommender systems such as shilling systems attack models and their defenses are discussed In addition recent topics such as learning to rank multi armed bandits gr

This book constitutes the refereed proceedings of the 19th International Conference on Theory and Applications of Satisfiability Testing SAT 2016 held in Bordeaux France in July 2016 The 31 regular papers 5 tool papers presented together with 3 invited talks were carefully reviewed and selected from 70 submissions The papers address different aspects of SAT including complexity satisfiability solving satisfiability applications satisfiability modulop theory beyond SAT quantified Boolean formula and dependency QBF

Author Ryan Sleeper one of the world s most qualified Tableau consultants complements his web posts and instructional videos with this guide to give you a firm understanding of how to use Tableau t