Examples of using Machine-learning model in English and their translations into Chinese
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The research also found that half of companies spend between eight to 90 days deploying a single machine-learning model.
Now, the City, the NRDC, the State, and AECOM are negotiating to return to the machine-learning model that was used in 2017.
It even includes a graphical user interface that may be employed to train a machine-learning model on a specific data collection.
Designing a machine-learning model for a certain task- such as image classification, disease diagnoses, and stock market prediction- is an arduous, time-consuming process.
A machine-learning model“learns” those correlations and, given a new material, can predict if the material is pure or tainted, and at what concentration.
A machine-learning model“learns” those correlations and, given a new material, can predict if the material is pure or tainted, and at what concentration.
A machine-learning model"learns" those correlations and, given a new material, can predict if the material is pure or tainted, and at what concentration.
A machine-learning model transforms its input data into meaningful outputs, a process that is“learned” from exposure to known examples of inputs and outputs.
It's an A.I. project: an attempt, Jeff says, to train a“giant” machine-learning model to do thousands, or millions, of different tasks.
Now a new machine-learning model that scans routinely collected NHS data has shown promising signs of being able to predict undiagnosed dementia in primary care.
So, although our work can fool a single machine-learning model, it does not imply that that fooling is enough to cause physical harm," he says.
Professors Matthias Preindl and Alan West, two Columbia professors, are developing a machine-learning model that can more accurately estimate a Li-Ion battery's charge level.
The higher-level Layers API is used to build machine-learning models on top of Core.
However, big data can help improve the accuracy of machine-learning models.
Such uncontrolled variables can be pernicious in machine-learning models.
Many other researchers have used machine-learning models to analyze data from biological experiments, by training an algorithm to generate predictions based on experimental data.
Black box machine-learning models are already having a major impact on some people's lives.
Researchers have discovered that some machine-learning models have difficulty detecting adversarial input- that is, data constructed specifically to deceive the model. .
Machine-learning models automatically adapt products to users' preferences, make recommendations for next steps and then suggest future features and products.
A large part of our research on software systems continues to relate to building machine-learning models and to TensorFlow in particular.