Search results
Results from the WOW.Com Content Network
In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters can be classified as either model hyperparameters (such as the topology and size of a neural network) or algorithm hyperparameters (such as the learning rate and the batch size of an optimizer).
Inception v2 was released in 2015, in a paper that is more famous for proposing batch normalization. [7] [8] It had 13.6 million parameters.It improves on Inception v1 by adding batch normalization, and removing dropout and local response normalization which they found became unnecessary when batch normalization is used.
While most approaches solely focus on finding architecture with maximal predictive performance, for most practical applications other objectives are relevant, such as memory consumption, model size or inference time (i.e., the time required to obtain a prediction). Because of that, researchers created a multi-objective search. [16] [20]
Google is releasing a new TensorFlow object detection API to make it easier for developers and researchers to identify objects within images. Google is trying to offer the best of simplicity and ...
There are many applications of U-Net in biomedical image segmentation, such as brain image segmentation (''BRATS'' [8]) and liver image segmentation ("siliver07" [9]) as well as protein binding site prediction. [10] U-Net implementations have also found use in the physical sciences, for example in the analysis of micrographs of materials.
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. [1]
Predict if a molecule, given the features, will be a musk or a non-musk. 168 features given for each molecule. 6598 Text Classification 1994 [237] Arris Pharmaceutical Corp. Steel Plates Faults Dataset Steel plates of 7 different types. 27 features given for each sample. 1941 Text Classification 2010 [238] Semeion Research Center