Abstract: Methods for preparing the Bruton's Tyrosine Kinase (“BTK”) inhibitor compound 2-{3?-hydroxymethyl-1-methyl-5-[5-((S)-2-methyl-4-oxetan-3-yl-piperazin-1-yl)-pyridin-2-ylamino]-6-oxo-1,6-dihydro-[3,4?]bipyridinyl-2?-yl}-7,7-dimethyl-3,4,7,8-tetrahydro-2H,6H-cyclopenta[4,5]pyrrolo[1,2-a]pyrazin-1-one are provided.

Abstract: The invention relates in particular to a process for the preparation of a compound of formula (I) wherein R1 and R2 are as defined in the description and in the claims.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for improving performance of a multi-class classifier. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by a multi-class classifier. The classifier's performance can be visualized using bidirectional bar graphs displaying true positives, false positives, and false negatives for each class.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for improving performance of a multi-class classifier. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by a multi-class classifier. The classifier's performance can be visualized using bidirectional bar graphs displaying true positives, false positives, and false negatives for each class.

Abstract: Domain-specific language understanding models that may be built, tested and improved quickly and efficiently are provided. Methods, systems and devices are provided that enable a developer to build user intent detection models, language entity extraction models, and language entity resolution models quickly and without specialized machine learning knowledge. These models may be built and implemented via single model systems that enable the models to be built in isolation or in an end-to-end pipeline system that enables the models to be built and improved in a simultaneous manner.