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wangex08's Projects

aiml-recommanding-system-project icon aiml-recommanding-system-project

A general movie recommending system involving python, numpy, sklearn, pandas. Applied feature-based modeling, content-based filtering, collaborative filtering and clustering method. Provided hybrid online/offline movie recommendations for anonymous users, new users (cold-start) and old users.

cv_interviews_q-a icon cv_interviews_q-a

CV算法岗知识点及面试问答汇总，主要分为计算机视觉、机器学习、图像处理和 C++基础四大块，一起努力向offers发起冲击！

data-science-competition icon data-science-competition

该仓库用于记录作者本人参加的各大数据科学竞赛的获奖方案源码以及一些新比赛的原创baseline. 主要涵盖：kaggle, 阿里天池，华为云大赛校园赛，百度aistudio，和鲸社区，datafountain等

fuzzy-c-means-algorithms icon fuzzy-c-means-algorithms

项目基于论文《Fuzzy c-Means Algorithms for Very Large Data》，使用Python语言实现FCM算法及其扩展算法，包括FCM、spFCM、oFCM、kFCM、reskFCM、spkFCM、okFCM。

modelarts-lab icon modelarts-lab

ModelArts开发者交流互动平台，@ModelArts服务官网：https://www.huaweicloud.com/product/modelarts.html

multiclassify_lstm_forchinese icon multiclassify_lstm_forchinese

本项目主要是利用LSTM来对中文文本进行情感分类，包含四个类别（愤怒，焦虑，抑郁，伤感）

recommendation-systems icon 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

swarm-intelligence-optimization-algorithm icon swarm-intelligence-optimization-algorithm

种群算法复现，包括乌鸦搜索(Crow Search Algorithm, CSA)、樽海鞘群算法(Salp Swarm Algorithm, SSA)、缎蓝园丁鸟优化算法(Satin Bowerbird Optimizer, SBO)、麻雀搜索算法(Sparrow Search Algorithm, SSA)、 狼群搜索算法(2007WPS, 2013WPA)、正余弦优化算法(Sine Cosine Algorithm, CSA）

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