首页|Findings from University College London (UCL) Broaden Understanding of Machine L earning (An Ultralow-power Real-time Machine Learning Based Fnirs Motion Artifac ts Detection)
Findings from University College London (UCL) Broaden Understanding of Machine L earning (An Ultralow-power Real-time Machine Learning Based Fnirs Motion Artifac ts Detection)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news reporting from London, United Kingdom, by NewsRx journalists, research stated, “Due to iterative matrix multiplicatio ns or gradient computations, machine learning modules often require a large amou nt of processing power and memory. As a result, they are often not feasible for use in wearable devices, which have limited processing power and memory.”
LondonUnited KingdomEuropeCyborgsEmerging TechnologiesMachine LearningUniversity College London (UCL)
NETL
NSTL
