Sentence paraphrase generation at the sentence level
-
Updated
Dec 7, 2022 - Python
Sentence paraphrase generation at the sentence level
⚛️ It is keras based implementation of siamese architecture using lstm encoders to compute text similarity
Recurrent Neural Networks (RNN, GRU, LSTM) and their Bidirectional versions (BiRNN, BiGRU, BiLSTM) for word & character level language modelling in Theano
Action Recognition in Video Sequences using Deep Bi-directional LSTM with CNN Features
A Keras multi-input multi-output LSTM-based RNN for object trajectory forecasting
This repo contains code written by MXNet for ocr tasks, which uses an cnn-lstm-ctc architecture to do text recognition.
AI: Deep Learning for Phishing URL Detection
Implement Human Activity Recognition in PyTorch using hybrid of LSTM, Bi-dir LSTM and Residual Network Models
Multitask learning: protein secondary structure prediction, b-values prediction and solvent-accessibility prediction
CRNN (CNN+RNN) for OCR using Keras / License Plate Recognition
Sequence-to-Sequence Generative Model for Sequential Recommender System
This is a solution to Cinnamon AI Challenge (https://drive.google.com/drive/folders/1Qa2YA6w6V5MaNV-qxqhsHHoYFRK5JB39) using convolutional, attention, bidirectional LSTM, achieving CER 0.081 WER 0.188 and SER 0.89
How Will Your Tweet Be Received? Predicting theSentiment Polarity of Tweet Replies
LSTM Model to predict BTCUSD ticker values
Train a bidirectional or normal LSTM recurrent neural network to generate text on a free GPU using any dataset. Just upload your text file and click run!
Generating text sequences using attention-based Bi-LSTM
Repo for Implementing Research Papers & Projects related to Machine Learning
Rough PyTorch implementation of "Action Recognition in Video Sequences using Deep Bi-directional LSTM with CNN Features" (Amin Ullah, et al.)
A bi-directional LSTM network for sequential prediction
Add a description, image, and links to the bidirectional-lstm topic page so that developers can more easily learn about it.
To associate your repository with the bidirectional-lstm topic, visit your repo's landing page and select "manage topics."