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bloossoms.github.io

个人主页

bootstrap-datepicker

A datepicker for twitter bootstrap (@twbs)

changehumanbackground

人物更换背景

devextreme-documentation

DevExtreme documentation

gitskills

grocery-recommendation-systems

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

hello-world

just another respository

jengine

JEngine是针对Unity开发者设计的开箱即用的框架，封装了强大的功能，小白也能快速上手，轻松制作可以热更新的游戏 | JEngine is a streamlined and easy-to-use framework designed for Unity Programmers which contains powerful features, beginners can start up quickly and making hot update-able games easily

jquery-tmpl

The original official jQuery Templates plugin. This project was maintained by the jQuery team as an official jQuery plugin. It is no longer in active development, and has been superseded by JsRender.

learn-javascript

JavaScript全栈教程参考源码→ http://t.cn/R2Elz3O 赞助→ http://t.cn/R5bhVpf

learn-sql

learngit-

movie-hybrid-recommendation-system-web-application

Regression-based Movie Recommender system that's a hybrid of content-based and collaborative filtering Recommender system Simply rate some movies and get immediate recommendations tailored for you

recommondation-system-demo

一些传统推荐算法的实现，包括基于内容的推荐，协同过滤，矩阵分解

tfidfcaculate

java编写的文本分词后利用tfidf计算每个文档的单词的tfidf值，并保存到文件中

vue-php

vue server side render with php

writetabledatabyjs

用JS把数据写入表格

xiecheng_hotel_reviews_spider

基于selenium的携程酒店评论爬取