PyTorch(1.6+) implementation of https://github.com/kang205/SASRec

Variational Auto-Encoders in a Sequential Setting.

https://github.com/JiachengLi1995/TiSASRec in PyTorch

Sequential recommendation algorithm

Source Code Generation Based On User Intention Using LSTM Networks

Generative model based HMM model

Hand Gesture Recognition using CNNs and Perceptrons in realtime (OpenCV)

Code for CIKM 2021 best short paper nomination "Modeling Sequences as Distributions with Uncertainty for Sequential Recommendation" https://arxiv.org/abs/2106.06165

the code of our paper "Beyond Matching: Modeling Two-Sided Multi-Behavioral Sequences For Dynamic Person-Job Fit" (实现十多个人岗匹配模型和动态人岗匹配模型的算法库，2021)

[arXiv'24] The official implementation code of LLMEmb

Data loader and model for variable length data in PyTorch

This repository contains Python code for generating a yawning detection model and using it to detect yawning instances from a live camera stream. The model architecture consists of convolutional and pooling layers, followed by fully connected layers.

Implementation of the paper 'Towards Full page Offline Bangla Handwritten Text Recognition using Image-to-Sequence Architecture'. For details, please read the README section.

Real time Face Mask Detection using Deep Learning & Machine Learning to recognize whether an individual is wearing a face mask or not. This was then used in conjunction with OpenCV facial recognition deep learning model to detect all faces and pass them to our Face Mask model for an assessment.

Sagemaker deployment of a pre-trained model for the classification of fake Instagram users.

CARCA: Context and Attribute-Aware Next-Item Recommendation via Cross-Attention

This repository contains Python code for generating a skin cancer detection model and utilizing it to detect skin cancer from user-inputted images or videos. The model architecture follows a sequential structure consisting of convolutional and pooling layers, with the final output layer using a sigmoid activation function.

A neural network model for predicting cryptocurrency prices using machine learning and time series analysis techniques.

In the current state of healthcare many hospitals are working over their capacity, and our group wanted to help fix this issue, we used machine learning to diagnose a healthy lung X-ray from a sick one.

This project aims to implement LSTM and GRU.