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gccrpm's Projects

movie-lens-movie-recommendation-system-using-deep-learning

This is a Deep learning model for recommendation systems with implementation in movie lens 100k and 1m database.

movie-recommendation-system

Our Deep Learning Model will predict which user is going to like which movie using the recommendation system using Boltzmann Machine

movie_recommendation_system

This is a movie recommendation system with tensorflow. Dataset is MovieLens.

movie_recommender

MovieLens based recommender system.使用MovieLens数据集训练的电影推荐系统。

music-recommendation-system-kaggle-ml-challenge-

Designed a classifier using various models such as Naïve bayes, SVM, NN, Deep learning, LGBM, XGBoost, etc. Performed pre-processing of data and applied k-fold cross-validation. Secured Top 100 position on Kaggle competition leaderboard with an accuracy of 69.62%.

namedentity_realtion_extraction

基于句法分析的命名实体关系抽取程序

ner

基于tensorflow深度学习的中文的命名实体识别

neural-attentive-item-similarity-model

TensorFlow Implementation of Neural Attentive Item Similarity Model for Recommendation on TKDE 2018

neural-collaborative-filtering-approach-for-movie-recommendation-system

This program implements Recommendation system using Neural Collaborative Filtering which uses a Deep learning for collaborative filtering

news_push_project

Real Time News Scraping and Recommendation System - React | Tensorflow | NLP | News Scrapers

njmere

neural joint model for entity and relation extraction

nlp_course

YSDA course in Natural Language Processing

nmt.matlab

Code to train state-of-the-art Neural Machine Translation systems.

nn_ner_tensorflow

Implementing , learning and re implementing "End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF" in Tensorflow

noise_mitigation

Noise Mitigation for Neural Entity Typing and Relation Extraction

openke

An Open-Source Package for Knowledge Embedding (KE)

openlearning4deeprecsys

Some deep learning based recsys for open learning.

opennre

Neural Relation Extraction implemented in TensorFlow

pagerank

Implementation of the PageRank algorithm

papers

算法相关的各种论文和slides

proofreadv1

中文文本自动纠错

pycorrector

pycorrector is a toolkit for text error correction. It was developed to facilitate the designing, comparing, and sharing of deep text error correction models.

pylibfm

libfm for python

pylouvain

A Python implementation of the Louvain method to find communities in large networks

python-100-days

Python - 100天从新手到大师

r-bert

Pytorch re-implementation of R-BERT model

recomendation-system

Design & Development of a Recommendation System for Goodreads & Development of a Multi-Label Classification Model from textual data using Deep Learning

recommendation-system

A Recommendation System based on Tensorflow using FNN and CNN.

recommendation-system-based-on-tensorflow

Neural candidates generation network based on Youtube reommender paper

recommender-system-for-movies-using-boltzmann-machine

From Amazon product suggestions to Netflix movie recommendations - good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet. I work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”. Final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender. Our f model Deep Belief Networks, complex Boltzmann Machines that will be covered for recommender system. The list of movies will be explicit so simply need to rate the movies you already watched, input your ratings in the dataset, execute model and voila! The Recommender System tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix!

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