Items tagged “deep learning”

6 results found


Overfitting is a problem in machine learning that introduces errors based on noise and meaningless data into prediction or classification. Overfitting tends to happen in cases where training data sets are either of insufficient size or training data sets include parameters and/or unrelated featu...

ImageNet dataset

The ImageNet is an extensive image database that has been instrumental in advancing computer vision and deep learning research. It contains more than 14 million, hand-annotated images classified into more than 20,000 categories. In at least one million of the images, bounding boxes are also prov...

Deep learning frameworks

Deep learning frameworks are instruments for training and validating deep neural networks, through high-level programming interfaces. Widely used deep learning frameworks include the libraries PyTorch, TensorFlow, and Keras. A programmer can use these libraries of higher functions to quickly de...

Learning curve (machine learning)

A learning curve is a plot of the learning performance of a machine learning model (usually measured as loss or accuracy) over time (usually in a number of epochs). Learning curves are a widely used diagnostic tool in machine learning to get an overview of the learning and generalization behavi...


Autoencoders are an unsupervised learning technique in which artificial neural networks are used to learn to produce a compressed representation of the input data. Essentially, autoencoding is a data compression algorithm where the compression and decompression functions are learned automatical...

CheckList for EvaluAtion of Radiomics research (CLEAR)

The CheckList for Evaluation of Radiomics Research (CLEAR) is a 58-item reporting guideline designed specifically for radiomics. It aims to improve the quality of reporting in radiomics research 1. CLEAR is endorsed by the European Society of Radiology (ESR) and the European Society of Medical I...

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