Leverage top XAI frameworks to explain your machine learning models with ease and discover best practices and guidelines to build scalable explainable ML systems Key Features • Explore various expla……続きを見る
著者：Daniil Ryabko
出版社： Springer International Publishing
発売日： 2019年03月09日
Stationarity is a very general, qualitative assumption, that can be assessed on the basis of application specifics. It is thus a rather attractive assumption to base statistical analysis on, especia……続きを見る
This book focuses on end-to-end robotic applications using vision and control algorithms, exposing its readers to design innovative solutions towards sensors-guided robotic bin-picking and assembly ……続きを見る
Emerging Technologies for Digital Infrastructure Development is a comprehensive and insightful book that reviews the transformative impact of cutting-edge technologies on the digital landscape. It p……続きを見る
Emerging Technologies for Digital Infrastructure Development is a comprehensive and insightful book that reviews the transformative impact of cutting-edge technologies on the digital landscape. It p……続きを見る
This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired perfor……続きを見る
This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to……続きを見る
Guide covering topics from machine learning, regression models, neural network to tensor flow Key features Machine learning in MATLAB using basic concepts and algorithms. Deriving and accessing of d……続きを見る
著者：Daniil Ryabko
出版社： Springer International Publishing
発売日： 2020年10月28日
The author considers the problem of sequential probability forecasting in the most general setting, where the observed data may exhibit an arbitrary form of stochastic dependence. All the results pr……続きを見る
