Abstract: Quantized neural networks significantly reduce storage requirements and computational complexity by lowering the numerical precision of weights and activations. Among these, binary neural ...

This jupyter notebook tutorial is meant to be a general introduction to machine and deep learning. We use seismic time series data from i) real earthquakes and ii) nuisance signals to train a suite of ...

A wave of pseudoscientific papers has tried to dismantle one of biology’s most fundamental truths: only two sexes exist, male and female. These papers often claim that sex is a broad “spectrum,” and ...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important: BCE weighs mistakes from both ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Biological sex is usually described in simple binary terms: male or female. This works well for germ cells (sperm versus eggs), but for other body organs it is of little help. A new study by the Max ...

Too Long; Didn't Read This tutorial walks you through fine-tuning a ResNet-18 model from TensorFlow’s Model Garden for classifying images in the CIFAR-10 dataset. You’ll learn how to set up the ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

i am running binary classification report. my "target" column is binary 0,1 values, "pred_lablel" is binary 01, values and "prediction" is probabilities between 0-1 i get auc/roc, log loss but ...