Classifying the sentiment of IMDb reviews by implementing long short-term memory networks

Time Series Forecasting with Neural Networks

Recurrent Neural Networks (RNN's) for sentiment analysis on IMDB dataset from keras....

Language process with Python

RNN, Word Embedding, LSTM, Classification

Audio-based Indonesia Toxic Language Classification using Recurrent Neural Network, Speech Recognition and Natural Language Processing.

Natural Language Processing | BRACU

Toy example of RNN LSTM in TensorFlow

Certificates of the Deep Learning Specialization from Coursera (2020)

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.

Create a neural network architecture to automatically generate captions from images.

This repo contains all the necessary files to give you an idea of the complexity of an undergrad capstone project.

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.

In this notebook, I implemented a recurrent neural network (Long short-term memory) using PyTorch that performs sentiment analysis.

Opinion recommendation is a task, recently introduced, for consistently generating a text review and a rating score that a certain user would give to a certain product, which has never seen before. Input information driving recommendation is text reviews and ratings for this product contributed by other users and text reviews submitted by the us…

This repository contains the code on how to train an LSTM network from scratch to generate text.

Deep Learning class projects from Kagle. All projects are individual projects conducted by me using pyhton (keras, tensor-flow, matplotlib and other libraries). Different Deep Neural Network (DNN) methods were used and results were compared based one efficiency and accuracy. Results and conclusions based on results were reported.

Simulation of "Triggering Proactive Business Process Adaptations via Online Reinforcement Learning" paper with proper steps and recreating the results

Tags: time series, forecast, prediction, convolutional layer, recurrent neural network (RNN), long short term memory (LSTM), Tensorflow, Tensorflow Data