Popular GitHub Repositories for Movie

A Deep Learning based project for creating line art portraits.

PALLAIDIUM - a generative AI movie studio integrated in the Blender Video Editor.

A self-hosted stack for media management and streaming, with AI-powered movie and show recommendations. Includes Sonarr, Radarr, qBitTorrent, Prowlarr, Jellyfin, Jellyseerr, Recommendarr, and VPN support.

基于seq2seq模型的简单对话系统的tf实现，具有embedding、attention、beam_search等功能，数据集是Cornell Movie Dialogs

本项目开源基于NextJS的前端， 希望能够提供一个用于生成式AI的文字转视频， 尤其是电影从编剧到视频生成的Web前端平台参考。Everyone can become a director. The Nextjs front-end of an AI driven platform for automatic movie/video generation (form GPT script generation to text2video movie generation).这是一个免费试用AI视频创作平台，集成了基于GPT的视频剧本生成和视频生成功能。 我们的理想是让每个人都能成为导演，以最快的方式将日常中的任何创意转化为高质量的视频， 无论是电影、营销视频、还是自媒体视频。

A step-by-step tutorial on developing a practical recommendation system (retrieval and ranking) using TensorFlow Recommenders and Keras.

SeerrBridge automates movie torrent fetching by integrating Jellyseer/Overseer with DebridMediaManager. It listens to OverSeerr movie requests via webhook and uses DMM / Real-Debrid for efficient downloads. With features like persistent browser sessions and queue management, it simplifies media handling. https://discord.gg/Dy5xNzEHKw

Simple Pinecone + OpenAI framework to do RAG on movie reviews (or anything else)

基于seq2seq模型的简单对话系统的tf实现，具有embedding、attention、beam_search等功能，数据集是Cornell Movie Dialogs

电影知识图谱，主要包括实体识别、实体查询、关系查询以及智能问答等。movie knowledge graph(Entity identification, graph display, and intelligent question and answer)

Data Science algorithms and topics that you must know. (Newly Designed) Recommender Systems, Decision Trees, K-Means, LDA, RFM-Segmentation, XGBoost in Python, R, and Scala.

The purpose of our research is to study reinforcement learning approaches to building a movie recommender system. We formulate the problem of interactive recommendation as a contextual multi-armed bandit.

A PyTorch CNN for classifying the sentiment of movie reviews, based on the paper "Convolutional Neural Networks for Sentence Classification" by Yoon Kim (2014).

This project walks through how you can create recommendations using Apache Spark machine learning. There are a number of jupyter notebooks that you can run on IBM Data Science Experience, and there a live demo of a movie recommendation web application you can interact with. The demo also uses IBM Message Hub (kafka) to push application events to topic where they are consumed by a spark streaming job running on IBM BigInsights (hadoop).

Content based movie recommendation system with sentiment analysis

This repository contains a DistilBERT model fine-tuned using the Hugging Face Transformers library on the IMDb movie review dataset. The model is trained for sentiment analysis, enabling the determination of sentiment polarity (positive or negative) within text reviews.

Naver movie review sentiment classification with KoBERT

使用MovieLens数据集实现了基于Auto Encoder(AE), Variational Auto Encoder(VAE), BERT的深度学习电影推荐系统

豆瓣电影爬虫——a crawler which is able to crawl movie detail and short comments, save them to database mysql, also include Sentiment analysis based on comments

Deep-Atrous-CNN-Text-Network: End-to-end word level model for sentiment analysis and other text classifications