  The photometric light curves of BRITE satellites were examined through a
machine learning technique to investigate whether there are possible exoplanets
moving around nearby bright stars. Focusing on different transit periods,
several convolutional neural networks were constructed to search for transit
candidates. The convolutional neural networks were trained with synthetic
transit signals combined with BRITE light curves until the accuracy rate was
higher than 99.7 $\%$. Our method could efficiently lead to a small number of
possible transit candidates. Among these ten candidates, two of them, HD37465,
and HD186882 systems, were followed up through future observations with a
higher priority. The codes of convolutional neural networks employed in this
study are publicly available at
http://www.phys.nthu.edu.tw/$\sim$jiang/BRITE2020YehJiangCNN.tar.gz.
