A字头's starred repositories
competition-baseline
数据挖掘、计算机视觉、自然语言处理、推荐系统竞赛知识、代码、思路
Deep-Learning-with-TensorFlow-book
深度学习入门开源书，基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
BayesianOptimization
A Python implementation of global optimization with gaussian processes.
auto-sklearn
Automated Machine Learning with scikit-learn
RelationPrediction
Implementation of R-GCNs for Relational Link Prediction
RottenTomatoesCNN
:tomato: Predicting the sentiment of Rotten Tomatoes movie reviews using deep learning.
Tensorflow_AgeGender_CNN
Tensorflow implements convolutional neural networks(CNN) to predict age and gender
MovieRecommenderSystem
In this project, I develop a collaborative filtering recommender (CFR) system for recommending movies. The basic idea of CFR systems is that, if two users share the same interests in the past, e.g. they liked the same book or the same movie, they will also have similar tastes in the future. If, for example, user A and user B have a similar purchase history and user A recently bought a book that user B has not yet seen, the basic idea is to propose this book to user B. The collaborative filtering approach considers only user preferences and does not take into account the features or contents of the items (books or movies) being recommended. In this project, in order to recommend movies, I used a large set of user’s preferences towards the movies from a movie rating dataset. The dataset used was from MovieLens, and is publicly available at http://grouplens.org/datasets/movielens/latest. The method used here was UBCF(User Based Collaborative Filter) and the Similarity Calculation Method was based on Cosine Similarity. The Nearest Neighbors was set to 30.The predicted item ratings of the user will be derived from the 5 nearest neighbors in its neighborhood. When the predicted item ratings are obtained, the top 10 most highly predicted ratings will be returned as the recommendations. The project involved various concepts such as k means clustering algorithm solution for recommending movies to users based on their selection on genre and movie interest. The front end was developed using Shiny package. Technology : R
LinkPrediction
Solution for Tsinghua Data Science Winter School 2017