Participants in this Specialization have the opportunity to construct and train various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers. They learn to enhance these networks with techniques such as Dropout, BatchNorm, Xavier/He initialization, among others.

Implementation of the model inversion attack on the Gated-Recurrent-Unit neural network

Algorithm to convert between various languages with the help of NLP techniques

Deep Learning Machine Learning Templates

Hotel review sentiment analysis using GRU (Gated Recurrent Unit) model

Microsoft Stock Deep Learning Model Prediction and Story Timeline Analysis.

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

Introductory-Gru

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

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

Implementation of LSTM time series tuned with GRU.

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.

Codes for EEE 443 Neural Networks Projects

Deep Learning in python

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

CoreML compatible GRU neural network for dynamic prediction

STM32F429 Online handwritten character classification with Gated Recurrent Unit Neural Network

Predict the next 24 hours' temperatures by GRU and Transformer