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.

:shield: A GRU deep learning system against attacks in Software Defined Networks (SDN).

A comprehensive collection of 35+ recurrent neural network layers for Flux.jl

Official implementation for "CONVIQT: Contrastive Video Quality Estimator"

Stock Price prediction for Yahoo Inc. using GRU (Gated Recurrant Units) in Keras. Predicting closing price for Yahoo stocks

A collection of 25+ PyTorch-compatible implementations of recurrent layers

Chatbot using Seq2Seq model and Attention

Construct a speech dataset and implement an algorithm for trigger word detection (sometimes also called keyword detection, or wakeword detection).

Bachelor's thesis carried at Universitat Politecnica de Catalunya in partial fullfilment of the requirements for the degree in Telecommunications Technologies and Services Engineering

Image classification using CNN

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

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.

Lake surface water evaporation modeling using remote-sensed water quality parameters (CHL, CDOM, TSM, temperature) and Bayesian-optimized LSTM/GRU hybrids validated against Penman-FAO.

Pytorch implementation of a GRU-based RNN for Sentiment Analysis in Mental Disorder Online Communitites.

Protein secondary structure prediction from amino acid sequence using machine learning

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.

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 code and data for probabilistic forecasting of electricity loads.

Recurrent Neural Network for generative MIDI music

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.

A RNN based voice application which can do tasks when it recognizes the user speaking the Trigger word. Here the trigger word is "activate".

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.

Automatically extracts goal highlights from football match videos using AI-powered ball tracking, scoreboard detection and crowd cheer analysis.

🔁Graphical models, Recurrent Neural Networks and SIFT algorithm for image processing, signal analysis and timeseries forecasting (MD Course: Intelligent Systems for Pattern Recognition)