Examples of using Machine learning framework in English and their translations into Chinese
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Our AI and machine learning framework, Adobe Sensei, takes our DMP to the next level.
Like Microsoft DMTK, Google TensorFlow is a machine learning framework designed to distribute computations within a cluster.
The Datumbox Machine Learning Framework is an open-source framework written in Java which allows the rapid development of Machine Learning and Statistical applications.
Initially released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy across a variety of platforms.
Mahout is an example of a machine learning framework that was popular on Apache Hadoop, while Apache Spark's MLlib library today has become a standard.
Much like Microsoft's DMTK, Google TensorFlow is a machine learning framework designed to scale across multiple nodes.
SystemML is a machine learning framework built with multiple backend support, including Apache Spark and Apache Hadoop.
Nabar's novel approach is to build a“meta” machine learning framework that automates the building of entire machine learning pipelines.
So it really depends on the details and it is completely application dependent, not machine learning framework dependent.
Moreover, they don't train machine learning models, but instead run inference with a lightweight version of Google's TensorFlow machine learning framework dubbed TensorFlow Lite.
Under active development since 2008, Encog is a machine learning framework created by data scientist Jeff Heaton.
Fully managed machine learning services use templates, pre-built models and/or drag-and-drop development tools to simplify and expedite the process of using a machine learning framework.
During its 2018 Worldwide Developers Conference in June, Apple introduced an improved version of ML Core, its on-device machine learning framework for iOS.
In 2016, the Google Brain team published a model for TensorFlow, Google's open source machine learning framework, that can generate single-line summarizations of news articles.
Initially released in 2017, Caffe(Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on expressiveness, speed, and modularity.
Initially released in 2017, Caffe(Convolutional Architecture for Fast Feature Embedding) is a machine learning framework that focuses on expressiveness, speed, and modularity.
He further said that“30- 40 percent” of SenseTime's research team is devoted to improving SenseTime's internal machine learning framework, Parrots, and improving SenseTime's computing infrastructure.
AI and Machine Learning Frameworks.
TensorFlow* is one of the leading deep learning and machine learning frameworks today.
Scikit-learn and Spark MLlib are machine learning frameworks.