全球的股票总市值正在骤降。4月减少了约8万亿美元（7.3％），单月的减少金额和降幅均创2020年3月新冠危机加深后、2年1个月以来的新低。在优先封控疫情的中国及加速收紧金融政策的美国，股价明显下跌，投资者越来越警惕世界经济的恶化风险。
Source: 全球总市值4月蒸发8万亿美元，中国降幅最大 日经中文网
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Yggdrasil is an early-stage implementation of a fully end-to-end encrypted IPv6 network. It is lightweight, self-arranging, supported on multiple platforms and allows pretty much any IPv6-capable application to communicate securely with other Yggdrasil nodes. Yggdrasil does not require you to have IPv6 Internet connectivity – it also works over IPv4.
The key to not having to deal with this problem anymore, for me, was starting to proactively switch things around to break the routine of consecutive work-weeks. One of my tricks was to do some kind of mini-vacation every 6-8 weeks, go somewhere new, leave work behind for 3-4 days. Even smaller things like regular social events can work wonders – anything that breaks the weekly routine.
deface is a simple command-line tool for automatic anonymization of faces in videos or photos. It works by first detecting all human faces in each video frame and then applying an anonymization filter (blurring or black boxes) on each detected face region. All audio tracks are discarded as well.
Source: GitHub – ORB-HD/deface: Video anonymization by face detection
- Back up data, make VM snapshots
- Prepare a Ubuntu LiveCD
- Boot from it
- Run GParted, and resize all the disks from there
- Remember to run fsck, or use GParted “check” feature
The overview speaks for itself. The highest frame rate measured comes from a Raspberry 64-bit OS overclocked to 1950 MHz. The lowest is the standard 32-bit Raspbian at 1500 MHz. Frame rates are only based on model run time (interpreter->Invoke()). Grabbing and preprocessing of an image are not taken into account, nor plotting output boxes or texts. This way, models working with pictures instead of video streams can also be measured. The actual frame rate will be slightly lower when playing video’s or using
Source: Deep learning examples on Raspberry 32/64 OS – Q-engineering
This page assists you to build your deep learning modal on a Raspberry Pi or an alternative like Google Coral or Jetson Nano. For more general information about deep learning and its limitations, please see deep learning. This page deals more with the general principles, so you have a good idea of how it works and on which board your network can run. Detailed step-by-step recipes for installing of software can be found at deep learning software for Raspberry Pi 4 and alternatives.
Source: Deep learning with Raspberry Pi and alternatives in 2022 – Q-engineering
In this article, I’m going to walk through the architectural implementations that led from the first MobileNet architecture to the state of the art EfficientNet architecture, with a bunch of improvements in between. The MobileNet architecture was aimed at reducing the computational requirements for large neural networks without a large drop in performance on the classification task. This was with the particular goal of deploying the networks on edge devices, like mobile phones. After several years of iterat
Source: ttumiel
Adding the simple bypass connections yielded an increase of 2.9 percentage-points in top-1 accuracy and 2.2 percentage-points in top-5 accuracy without increasing model size.
Source: Review: SqueezeNet (Image Classification) | by Sik-Ho Tsang | Towards Data Science
Scroll to the bottom of the page to see reviews of Neural Networks.