Pinned Repositories
ad_optimization
Kanalyzers: A Machine Learning Based Advertisement Optimization Web Tool
Advertisement-clicks
With Advertisement Dataset check whether or not a particular internet user clicked on an Advertisement on a company website
AdvSentEval
This project aims at creating adverserial examples for some baseline sentence embedding models.
aicoco
“爱可可-爱生活”微博内容精选
AIR-Tree
Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
Autogenerate_CC
awesome-deep-reinforcement-learning-in-finance
🔬 A collection for those AI (RL / DL / SL / Evoluation / Genetic Algorithm) used in financial market. otherwise, we add Technology Analysis / Alpha Research / Arbitrage and other useful strategies tools & docs in quantitative finance market.
awesome-monte-carlo-tree-search-papers
A curated list of Monte Carlo tree search papers with implementations.
download
🔴蓝灯最新版本下载 https://github.com/getlantern/download 🔴 Lantern Latest Download https://github.com/getlantern/lantern/releases/tag/latest 🔴
lipanpanpanpan's Repositories
lipanpanpanpan/Advertisement-clicks
With Advertisement Dataset check whether or not a particular internet user clicked on an Advertisement on a company website
lipanpanpanpan/AdvSentEval
This project aims at creating adverserial examples for some baseline sentence embedding models.
lipanpanpanpan/AIR-Tree
lipanpanpanpan/Algorithm_Interview_Notes-Chinese
2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记
lipanpanpanpan/Conditional-GANs-Pytorch
CGAN ProjectionCGAN ACGAN InfoGAN Pytorch
lipanpanpanpan/cubesviewer
Explore and visualize analytical datasets
lipanpanpanpan/DataMining-Project
Twitter project with topic: Stock Prediction with Neural Network, SVM, etc.
lipanpanpanpan/DeepEye
a source code for automatic data visualization and recommendation
lipanpanpanpan/DeepSemanticMatching
Paper list on deep semantic matching for search and recommendation
lipanpanpanpan/DIVE-frontend
Codebase for DIVE SPA using React and Redux
lipanpanpanpan/hashedcubes
Hashedcubes: Simple, Low Memory, Real-Time Visual Exploration of Big Data
lipanpanpanpan/IR_tool
The is a tool collection from my IR experiments
lipanpanpanpan/keywordquery
lipanpanpanpan/map-markers
lipanpanpanpan/PeerRead
Data and code for Kang et al., NAACL 2018's paper titled "A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications"
lipanpanpanpan/pytorch-GAN-CGAN
pytorch implementation of GAN and Conditional GAN
lipanpanpanpan/PyTorch_Tutorial
《Pytorch模型训练实用教程》中配套代码
lipanpanpanpan/R-tree-based-skyline-process
DEC indexing Rtree
lipanpanpanpan/road-network
QuadTree Model for generating random road network
lipanpanpanpan/RoadNetworkExtraction-MoveHack
Submission for MoveHack Global Mobility Hackathon 2018
lipanpanpanpan/s2s
s2s学习
lipanpanpanpan/s2s_tutorial
Sequence to Sqeuence Learning tutorial in Pytorch and more/ Bytecup Competition
lipanpanpanpan/SearchAndRecommendationSystem
We wanted to build a search and recommendation system for user queries related to local businesses. The main goal is to fulfil the need of the user and satisfy the user's query with corresponding matching results. We have built a generic search infrastrucure that can be extened to any domain but for the purpose of demonstration and for the purpose of this project we have limited the scope to Restaurants entities in state of Pennsylvania.
lipanpanpanpan/ssfree
provide shadowsocks free tutorial and free account
lipanpanpanpan/Text-Classification-Pytorch
Text classification using deep learning models in Pytorch
lipanpanpanpan/text_generate
通过char_RNN、VAE、GAN进行文本生成
lipanpanpanpan/TorchCGAN
CGAN with PyTorch
lipanpanpanpan/tornadostreaming
lipanpanpanpan/Traffic-Markers
Traffic Markers is an app that allows you to drop markers at your locations on the map.
lipanpanpanpan/YouTube-Popularity-prediction-using-Sentiment-Analysis-and-YouTube-API
Classifying the category of any online content and predicting its popularity is an important task for a wide range of systems, from advertising to recommendation systems or making profit and earn money from online content. We here present a system for categorising a video into three broad categories namely ‘Health and Fitness’, ‘Travel and Education’ and ‘Personality Development’, and then we predict the future popularity of video using regression method based on views from the past. We prove the results of our system against a dataset containing views of 1500 videos on YouTube with a mean error of 0.8.