Pinned Repositories
3d
Three.js 3D项目，包含冰墩墩🐼、数字城市🏙、3D人像👤、车模展示🚗、塞尔达传说🗡等一些3D趣味演示页面，持续优化中...访问链接如下👇
3DMaterials
BookRecSys
图书推荐系统
cdmf
Hicss best paper nominated: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation
deep_learning-1
Basic Deep Learning
knowledge_extraction
Chinese Open Entity Relation Extraction
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!
Red-Things
IOT(internet of things) platform based on Node-Red, Easy but very useful to built a iot system
stock-knowledge-graph
one stock knowledge graph project
StockPricePredictionFromFinancialFilings
Stock Price Predictions from Financial Statements using Machine Learning and Deep Learning algorithms augmented with Knowledge Graph Embeddings
gccrpm's Repositories
gccrpm/deep_learning-1
Basic Deep Learning
gccrpm/book
Deep Learning 101 with PaddlePaddle （深度学习框架入门教程）
gccrpm/chatbot
一个可以自己进行训练的中文聊天机器人， 根据自己的语料训练出自己想要的聊天机器人，可以用于智能客服、在线问答、智能聊天等场景。加入seqGAN版本。
gccrpm/chatbot-retrieval
Dual LSTM Encoder for Dialog Response Generation
gccrpm/CustomerServiceAI
智能客服
gccrpm/Deep-Learning-21-Examples
《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码
gccrpm/Deep-Learning-for-Recommendation-Systems
This repository contains Deep Learning based articles , paper and repositories for Recommender Systems
gccrpm/deeplearning
深度学习相关的模型训练、评估和预测相关代码
gccrpm/DeepSpeechRecognition
A Chinese Deep Speech Recognition System 包括基于深度学习的声学模型和基于深度学习的语言模型
gccrpm/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
Joint Extraction of Entities and Relations Based on cnn+rnn
gccrpm/librec
LibRec: A Leading Java Library for Recommender Systems, see
gccrpm/MachineLearning
gccrpm/models
Models and examples built with TensorFlow
gccrpm/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.
gccrpm/Recommendation-System-based-on-TensorFlow
Neural candidates generation network based on Youtube reommender paper
gccrpm/recsys_core
gccrpm/recsys_etl
ETL
gccrpm/recsys_model
gccrpm/recsys_spider
Spider
gccrpm/recsys_sql
SQL
gccrpm/recsys_ui
recsys-UI
gccrpm/Self-Attention-GAN-Tensorflow
Simple Tensorflow implementation of "Self-Attention Generative Adversarial Networks" (SAGAN)
gccrpm/Semantic
语义理解/口语理解，项目包含有词法分析：中文分词、词性标注、命名实体识别；口语理解：领域分类、槽填充、意图识别。
gccrpm/skip-gram-Chinese
skip-gram for Chinese word2vec base on tensorflow
gccrpm/subword-nmt
Unsupervised Word Segmentation for Neural Machine Translation and Text Generation
gccrpm/Tap-News
Real Time News Scraping and Recommendation System
gccrpm/technologyx
Learning notes of Machine Learn & Deep Learning
gccrpm/tensorflow_practice
tensorflow实战练习，包括强化学习、推荐系统、nlp等
gccrpm/transformer
A TensorFlow Implementation of the Transformer: Attention Is All You Need
gccrpm/yueye
Code for my blog: