Popular GitHub Repositories for Surprise

"Real-Time-Face-Emotion-Detection-Application" is a publicly available project aimed at detecting facial expressions in real-time. Leveraging advanced computer vision techniques, it accurately identifies emotions such as happiness, sadness, anger, and surprise from live video feeds. With its intuitive interface,

PILF: A IPWT-inspired continual learning experiment focus on mitigate catastrophic forgetting with Surprise-gated Mixture of Experts (MoE) model.

An AutoRecSys library for Surprise. Automate algorithm selection and hyperparameter tuning :rocket:

Use Bi-LSTM neural network to classify Chinese text sentiment, including eight categories (like, disgust, happiness, sadness, anger, surprise, fear, none)

Recommender Systems are the systems designed to that are designed to recommend things to the user based on many different factors. These systems predict the most likely product that the users are most likely to purchase and are of interest to. Recommendations typically speed up searches and make it easier for users to access content they’re interested in, and surprise them with offers they would have never searched for. In this project work, we explore the use of Reinforcement Learning based techniques to solve the problem of Movie Recommendation. We have implemented the following strategies: Multi Armed Bandits based recommender and an Actor-Critic based recommender framework using Deep Reinforcement Learning.

Given a voice detecting emotion it holds. Such as - Angry, Happy, Sad, Disgust, Neutral, Surprise & Pleasant. Etc...

Recommendation system for inter-related content. Uses natural language processing and collaborative filtering. Provides recommendations for books, movies, tvshows

Repository of OpenClassrooms' AI Engineer path, project #9 : create a books recommandation system, integrate and deploy it as a mobile app

Recommender system with Netflix database using matrix factorization

Aplicación de sistemas de recomendación en entornos educativos.

This is a learning repository about Databricks and Recommendation Systems

This project presents a real-time face emotion detection system implemented as a full-stack desktop application. The system integrates deep learning techniques to accurately classify human emotions, including happiness, sadness, anger, surprise, disgust, fear, and neutral expressions, from live video streams or camera inputs. Download apk file :-

Quantifying surprise in clinical care

A FinOps control plane for AI APIs - Drop-in API gateway that enforces hard budgets, rate limits, and custom policy checks before any request reaches AI providers. Stop surprise AI bills with multi-tenant budget controls, complete audit trails, and sub-20ms latency overhead

building a model to classify the emotion of an image face: Angry, Disgust, Fear, happy, Sad, Surprise, Neutral.

Emotion and Facial Key-Point Detection Classify emotions and detect facial key-points using deep learning! This project combines CNNs and Residual Blocks to predict 15 facial key-points and categorize facial expressions into five emotions: Angry, Disgust, Sad, Happy, and Surprise.

This project is a CNN-based facial expression recognition system that can identify seven basic emotions in images: Anger, Disgust, Fear, Happiness, Neutrality, Sadness, and Surprise. The system works by analyzing the facial features of individuals, such as the eyes, eyebrows, and mouth.

This project implements a Speech Emotion Recognition (SER) model using TensorFlow Lite, specifically designed for deployment on microcontrollers like the Arduino Nano BLE33. The model is trained on the RAVDESS dataset and can recognize seven emotions: Angry, Disgust, Fear, Happy, Neutral, Sad, and Surprise.

Collaborative filtering based book recommendation system