Patient2Vec: A Personalized Interpretable Deep Representation of the Longitudinal Electronic Health Record

Named Entity Recognition (NER) with different combinations of BiGRU, Self-Attention and CRF

Implementing different RNN models (LSTM,GRU) & Convolution models (Conv1D, Conv2D) on a subset of Amazon Reviews data with TensorFlow on Python 3. A sentiment analysis project.

Sequence Models repository for all projects and programming assignments of Course 5 of 5 of the Deep Learning Specialization offered on Coursera and taught by Andrew Ng, covering topics such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), Natural Language Processing, Word Embeddings and Attention Model.

Predict the next 24 hours' temperatures by GRU and Transformer

Named Entity Recognition - Python - Keras

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

STM32F429 Online handwritten character classification with Gated Recurrent Unit Neural Network

CoreML compatible GRU neural network for dynamic prediction

Created AI models to forecast Wallmart's sales. Used different models, like dense, LSTM, GRU and naive model. Different window and horizon sizes are used too. Compared models visually at the end.

Deep Learning in python

Recurrent neural network with GRUs for trigger word detection from an audio clip

Python library and tools for online handwritten Chinese character recognition

This repository includes course assignments of Natural Language Processing in TensorFlow on Coursera by DeepLearning.AI

Codes for EEE 443 Neural Networks Projects

Code for Multi-dimensional Gated Recurrent Units for the Segmentation of Biomedical Data

I constructed a knowledge graph of stakeholders of Bavarian state ministries and used network analysis to calculate statistics. Furthermore time-series feature forecasting and topological link prediction was employed to analyze the evolution of the network.

Implementation of LSTM time series tuned with GRU.

This repository contains notes, slides, labs, assignments and projects for the Deep Learning Specialization by DeepLearning.AI and Coursera.