Search Results for "task"

DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc.

SageMaker Hugging Face Inference Toolkit is an open-source library for serving Transformers models on Amazon SageMaker. This library provides default pre-processing, predict and postprocessing for certain Transformers models and tasks. It utilizes the SageMaker Inference Toolkit for starting up the model server, which is responsible for handling inference requests. For the Dockerfiles used for building SageMaker Hugging Face Containers, see AWS Deep Learning Containers. The SageMaker Hugging...

...It is simple, user-friendly, pragmatic and not limited to IT. 100% triple-centric and semantically-sound. Defines a total of 24 viewpoints. The ones needed are selected by taking the task sponsor's concerns and matching them to the typical concerns that each TRAK viewpoint addresses. The triples that address each concern are defined in the TRAK metamodel (https://sf.net/p/trakmetamodel). The mapping between metamodel triple and architecture viewpoint is held with a Neo4J graph model (https://doi.org/10.1002/eng2.12168) and defined using (more) architecture viewpoints (https://www.researchgate.net/publication/335176248_Architecture_Description_Viewpoints_Metamodel_Description_Implementation_and_Model_Changes). ...

...Maybe it can become your "black technology" will improve development productivity by 1.5 to 5 times, Zero coupling to your project and without changing anything. Normal code generator flow: Template → Source Code Jmr development automation flow: Template/Model/Action → Task → Source Code/Test Case/Document Template: Support Java Script(<%%> JSP-like) and Jmr Tag(set/get/for...) Model: Database Model(like Table/View), Java POJO Model, Xml File Model and includes Transform/Mapping/Convert Tool Action: A Java Class can set Java variables to template. Support any kind of project Maven, Gradle, Web, Java, Android, PHP, Python etc. ...

Integrates the Salesforce Force.com API into Apache Velocity. Provides an Ant task and a number of Velocity Tools for use within the template language. Templates can be written to generate Code, HTML, Data Loader scripts etc from Custom Object metadata.

System for generation of interactive help using predefined task model.