regulus-leonis Goto Github PK

followers: 0.0 following: 2.0 repos: 32.0 gists: 0.0

Name: Regulus

Type: User

Company: Hust

Location: China

Regulus's Projects

alexa-avs-sample-app

This project demonstrates how to access and test the Alexa Voice Service using a Java client (running on a Raspberry Pi), and a Node.js server.

I am pleased to update the AlexNet and its variants to adapt to TensorFlow 2.0/2.1/2.2/2.3. Since the original script of myalexnet_forward written by the AlexNet team was build on TensorFlow 1.x in 2017, it has generated many errors during the runtime in the env. of TensorFlow 2.x. Either the AlexNet team has no time to update, or it is very hard for developers to find out the AlexNet variants to adapt to TensorFlow 2.0/2.1/2.2 with the Google search. Furthermore, I split the AlexNet model in both the OOP and the pure command style and make client applications to call the AlexNet model because the AlexNet model is not separated in most of the published scenarios which incur the difficulties for developer's deep learning. Considering that AlexNet is the greatest milestone in the CNNN/DNN history (Mr. Hinton has just won the Turing Award in 2019 and the event is not far from fading away), it is important to continue its usability and accessibility to all developers. It is exciting for me to take the opportunity to share the new update.

better-cloudflare-ip

原作者已被删库，请珍惜使用

cloudflare

demo

electron-ssr-backup

electron-ssr原作者删除了这个伟大的项目，故备份了下来，不继续开发,且用且珍惜

fcn

free connect your private network from anywhere

frp

global-one-shot-pruning

An official implementation of the paper "How Sparse Can We Prune a Deep Network: A Geometric Viewpoint".

gobang_ai

基于博弈树α-β剪枝搜索的五子棋AI

haextra

Home Assistant Extras (Dashboard+AliGenie+...)

ipv6.tsinghua.edu.cn

kcptun

A Stable & Secure Tunnel based on KCP with N:M multiplexing and FEC. Available for ARM, MIPS, 386 and AMD64

lossless_compression

We propose a lossless compression algorithm based on the NTK matrix for DNN. The compressed network yields asymptotically the same NTK as the original (dense and unquantized) network, with its weights and activations taking values only in {0, 1, -1} up to scaling.