hookk

hookk's Stars

• YuanchenBei/Awesome-Large-Scale-Graph-Learning

  A curated list of papers on large-scale graph learning.

  121

• rapidsai/cudf

  cuDF - GPU DataFrame Library

  Language:C++8.4k900

• BUAA-CI-LAB/Literatures-on-GNN-Acceleration

  A reading list for deep graph learning acceleration.

  22018

• chwan1016/awesome-gnn-systems

  A list of awesome GNN systems.

  Language:Python28325

• FPGAwars/apio

  :seedling: Open source ecosystem for open FPGA boards

  Language:Verilog796136

• YosysHQ/yosys

  Yosys Open SYnthesis Suite

  Language:C++3.5k888

• m3y54m/FPGA-ASIC-Roadmap

  A roadmap for those who want to build a career as an FPGA / ASIC Engineer

  22124

• ermaozi/get_subscribe

  ✈️ 免费机场 / 免费VPN -> 自动获取免 clash/v2ray/trojan/sr/ssr 订阅链接，间隔12小时持续更新 | 科学上网 | 翻墙

  Language:Python6.9k596

• itewqq/MathF

  MathF introduction and issues.

  Language:HTML913

• AoEiuV020/rc

  Language:Shell115

• XueFuzhao/awesome-mixture-of-experts

  A collection of AWESOME things about mixture-of-experts

  95972

• dave1010/pandora

  ChatGPT Coding Unleashed! Pandora gives ChatGPT the ability to read and write files and run commands on your machine.

  Language:PHP10914

• WolfgangErb/GraphWedgelets

  Graph wedgelets for image compression

  Language:MATLAB11

• UCD4IDS/MultiscaleGraphSignalTransforms.jl

  MultiscaleGraphSignalTransforms.jl is a collection of software tools written in the Julia programming language for graph signal processing including HGLET, GHWT, eGHWT, NGWP, Lapped NGWP, and Lapped HGLET. Some of them were originally written in MATLAB by Jeff Irion, but we added more functionalities, e.g., eGHWT, NGWP, etc.

  Language:Julia93

• MahdiyarMM/MSG-CapsGAN

  KERAS implementation of the First Multi-Scale Gradient Capsule GAN for Super-Resolution

  Language:Python72

• RUCAIBox/RecBole

  A unified, comprehensive and efficient recommendation library

  Language:Python3.4k612

• cchao0116/CTSMA-ICML21

  Code for ICML21 paper "Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation"

  Language:Python112

• seniorporwal/MovieRecommendationSystem

  ABSTRACT OF THE PROJECT:- A Recommendation engine filters the data using different algorithms and recommends the most relevant items to users. It is a type of information filtering system which attempts to predict the preferences of a user, and make suggests based on these preferences, especially in streaming services. For streaming services like Netflix, recommendation systems are essential for helping users find new movies to enjoy. Objective of the recommendation system is to achieve customer loyalty by providing relevant content and maximising the time spent by a user on your website or channel. This also helps in increasing customer engagement. Three main approaches are used for recommender systems. One is Demographic Filtering i.e They offer generalized recommendations to every user, based on movie popularity with similar demographic features. Second is Content-based filtering, where users interests are profiled using information collected, and recommend items based on that profile. The other is collaborative filtering, where we try to group similar users together and use information about the group to make recommendations to the user. In this project, we propose a machine learning approach to produce a Content-based filtering system which predicts movie recommendations for a user based on large database of continuously updated movies. Need of Movie Recommendation System – Helps the item provider (ex. Netflix/Amazon) to deliver their items to the right user – Websites like Netflix can improve user-engagement – It increases revenues for business through increased consumption.

  Language:Jupyter Notebook11

• hidasib/GRU4Rec

  GRU4Rec is the original Theano implementation of the algorithm in "Session-based Recommendations with Recurrent Neural Networks" paper, published at ICLR 2016 and its follow-up "Recurrent Neural Networks with Top-k Gains for Session-based Recommendations". The code is optimized for execution on the GPU.

  Language:Python762224

• JindongGu/GraCapsNet

  A pytorch implementation of the AAAI2021 paper GraCapsNet: Interpretable Graph Capsule Networks for Object Recognition

  Language:Python91

• demorenoc/springer-books

  An scrappeR of Springer books

  Language:R369

• Wang-Shuo/GraphRec_PyTorch

  A PyTorch implementation of Graph Neural Networks for Social Recommendation (GraphRec)

  Language:Python19048

• chenchongthu/SAMN

  This is our implementation of SAMN: Social Attentional Memory Network

  Language:Python4713

• wenqifan03/GraphRec-WWW19

  Graph Neural Networks for Social Recommendation, WWW'19

  Language:Python361109

• hongleizhang/RSAlgorithms

  Some algorithms about traditional and social recommendation.

  Language:Python704203

• hihiee/pinsage

  Python package built to ease deep learning on graph, on top of existing DL frameworks.

  2

• Enigmatisms/CapsNet

  My own implementation of CVPR 2017 paper: Dynamic Routing Between Capsules

  Language:Python4

• moejoe95/res-capsnet

  Official implementation of the paper "Training Deep Capsule Networks with ResidualConnections".

  Language:Python82

• DeepMatrixCapsules/DeepMatrixCapsules

  Deep Matrix Capsules Implementation

  Language:Jupyter Notebook145

• amitkumarj441/CapsRoute_NER

  Capsule Routing for Named Entity Recognition

  Language:Jupyter Notebook105