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jinhuli11's Projects

byteps icon byteps

A high performance and general PS framework for distributed training

chat icon chat

基于自然语言理解与机器学习的聊天机器人,支持多用户并发及自定义多轮对话

chinese-poetry icon chinese-poetry

最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人,21050首词。

coach icon coach

Reinforcement Learning Coach by Intel AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

cointrol icon cointrol

฿ Bitcoin trading bot with a real-time dashboard for Bitstamp.

crawl-zsxq icon crawl-zsxq

爬取知识星球,并制作成 PDF 电子书。

csp icon csp

High-level Semantic Feature Detection: A New Perspective for Pedestrian Detection, CVPR, 2019

deep-learning icon deep-learning

:computer:Tensorflow实战:实战教程、’手写数字识别、Discuz验证码识别

deepnude-an-image-to-image-technology icon deepnude-an-image-to-image-technology

DeepNude's pix2pixHD algorithms(proposed by NVIDIA) and general-purpose Image-to-Image theory and practice research. DeepNude的pix2pixHD算法(英伟达提出)以及通用的Image-to-Image理论与实践研究。

deepnude_nowatermark_withmodel icon deepnude_nowatermark_withmodel

DeepNude源码,去水印,带三个模型文件下载地址 (cm.lib, mm.lib, mn.lib), 附使用说明,离线运行,下载即跑,基于GAN的一行命令给小姐姐"换"衣服。

deeprl icon deeprl

【深度强化学习社区】一个资料与学习内容最全的服务平台

deepspeed icon deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective.

distribute_crawler icon distribute_crawler

使用scrapy,redis, mongodb,graphite实现的一个分布式网络爬虫,底层存储mongodb集群,分布式使用redis实现,爬虫状态显示使用graphite实现

dive-into-dl-tensorflow2.0 icon dive-into-dl-tensorflow2.0

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现,项目已得到李沐老师的同意

dlquant icon dlquant

Applying Deep Learning and NLP in Quantitative Trading

dongfang icon dongfang

dongfang是一个基于Scrapy-Selenium的股票信息爬虫,爬取了东方网千股千评的所有股票信息。

edge-intelligence icon edge-intelligence

随着移动云计算和边缘计算的快速发展,以及人工智能的广泛应用,产生了边缘智能(Edge Intelligence)的概念。深度神经网络(例如CNN)已被广泛应用于移动智能应用程序中,但是移动设备有限的存储和计算资源无法满足深度神经网络计算的需求。神经网络压缩与加速技术可以加速神经网络的计算,例如剪枝、量化、卷积核分解等。但是这些技术在实际应用非常复杂,并且可能导致模型精度的下降。在移动云计算或边缘计算中,任务卸载技术可以突破移动终端的资源限制,减轻移动设备的计算负载并提高任务处理效率。通过任务卸载技术优化深度神经网络成为边缘智能研究中的新方向。Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge这篇文章提出了协同推断的**,将深度神经网络进行分区,一部分层在移动端计算,而另一部分在云端计算。根据硬件平台、无线网络以及服务器负载等因素实现动态分区,降低时延以及能耗。本项目给出了边缘智能方面的相关论文,并且给出了一个Python语言实现的卷积神经网络协同推断实验平台。关键词:边缘智能(Edge Intelligence),计算卸载(Computing Offloading),CNN模型分区(CNN Partition),协同推断(Collaborative Inference),移动云计算(Mobile Cloud Computing)

elitequant icon elitequant

A list of online resources for quantitative modeling, trading, portfolio management

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