There are 0 repository under gated-recurrent-units topic.
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine for Malware Classification
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.
Comparing Long Term Short Memory (LSTM) & Gated Re-current Unit (GRU) during forecasting of oil price .Exploring multivariate relationships between West Texas Intermediate and S&P 500, Dow Jones Utility Avg, US Dollar Index Futures , US 10 Yr Treasury Bonds , Gold Futures.
THEANO-KALDI-RNNs is a project implementing various Recurrent Neural Networks (RNNs) for RNN-HMM speech recognition. The Theano Code is coupled with the Kaldi decoder.
Stock Price prediction for Yahoo Inc. using GRU (Gated Recurrant Units) in Keras. Predicting closing price for Yahoo stocks
Bachelor's thesis carried at Universitat Politecnica de Catalunya in partial fullfilment of the requirements for the degree in Telecommunications Technologies and Services Engineering
Construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection).
Chatbot using Seq2Seq model and Attention
Image classification using CNN
:shield: A GRU deep learning system against attacks in Software Defined Networks (SDN).
Curated implementation notebooks and scripts of deep learning based natural language processing tasks and challenges in TensorFlow.
Gated Recurrent Unit implementation from scratch
doctor_prescription_recognization_using_DeepLearning project for epics
Image captioning with a benchmark of CNN-based encoder and GRU-based inject-type (init-inject, pre-inject, par-inject) and merge decoder architectures
It analyses the movie review entered by a user for any specific movie and analyses what is the sentiment of the review. It helps the companies rate the movie and understand crowd sentiment regarding it. Sentiment analysis is a natural language processing problem where text is understood and the underlying intent is predicted.
An implementation of classical GRU (Cho, el at. 2014) along with Optimized versions (Dey, Rahul. 2017) on TensorFlow that outperforms Native tf.keras.layers.GRU(units) implementation of Keras.
ABSA is Aspect Based Sentiment Analysis which is a fine-grained Sentiment Analysis. This is achieved using a sequential Recurrent Neural Network called the Bidirectional Gated Recurrent Unit. This model predicts the aspect category and the sentiment class given a laptop review.
This repository contains code and data for probabilistic forecasting of electricity loads.
Pytorch implementation of a GRU-based RNN for Sentiment Analysis in Mental Disorder Online Communitites.
With an ever-increasing amount of astronomical data being collected, manual classification has become obsolete; and machine learning is the only way forward. Keeping this in mind, the LSST Team hosted the PLAsTiCC in 2018. This repository details our approach to this problem.
This repository contains Jupyter Notebook Files of some state of the art projects that I completed during my internship program in deeplearning.ai. The project files are divided into 5 main categories or respective courses that the deeplearning.ai provides.
A RNN based voice application which can do tasks when it recognizes the user speaking the Trigger word. Here the trigger word is "activate".
bike sharing prediction using recurrent neural network (RNN)-gated recurrent unit (GRU) implemented in python using the pytorch framework
🔁Graphical models, Recurrent Neural Networks and SIFT algorithm for image processing, signal analysis and timeseries forecasting (MD Course: Intelligent Systems for Pattern Recognition)
A deep learning based application which is entitled to help the visually impaired people. The application automatically generates the textual description of what's happening in front of the camera and conveys it to person through audio. It is capable of recognising faces and tell user whether a known person is present in front of him or not.
A simple figure of speech classifier made in a jupyter notebok using keras. Gated Recurrent Units are used inplace of LSTM's becuase of little data.
Triplet Loss Based User Analysis
👨🏻💻 My own repository to explore LearnQuran tech product in particular -obviously- AI stuffs
Sentiment analysis of Covid-19 tweets using XGBoost, LSTM and BERT
Estimating the growth or depreciation on exchange rates by using sentiment analysis method from social media comments
We use machine learning tools to predict the price of ethereum from historical data, economic indicators, and community sentiment on ethereum specifically from twitter.
Developing a PyTorch-based solution for predicting future values in financial time series data, leveraging RNNs and GRUs as part of the M3 competition for time series forecasting.
Generated text using RNN(Gate Recurrent Unit: GRU).