<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/f72cd0d666284853809f8ce85dcee246&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/f72cd0d666284853809f8ce85dcee246-8c60002a6d9d6fab.gif</thumbnail_url><duration>86.109</duration><title>Automating Attendance Management with Deep Learning</title><description>Hi everyone, I wanted to share my automated solution for managing employee attendance, which I&apos;ve developed to streamline the process. It utilizes a deep learning pipeline that includes MT-CNN for face detection and Inception ResNet V1 for generating high-dimensional embeddings. This approach not only improves accuracy but also enhances efficiency compared to manual methods. I encourage you all to consider how this technology could be integrated into our current systems. Your feedback and thoughts on implementation would be greatly appreciated!</description></oembed>