bahdanau-attention

This repository contain various types of attention mechanism like Bahdanau , Soft attention , Additive Attention , Hierarchical Attention etc in Pytorch, Tensorflow, Keras

Configurable Encoder-Decoder Sequence-to-Sequence model. Built with TensorFlow.

Chapter 9: Attention and Memory Augmented Networks

Image Captioning is the process of generating textual description of an image. It uses both Natural Language Processing and Computer Vision to generate the captions.

Tensorflow 2.0 tutorials for RNN based architectures for textual problems

Seq2Seq model implemented with pytorch, using Bahdanau Attention and Luong Attention.

A multi-layer bidirectional seq-2-seq chatbot with bahdanau attention.

Implementation of GRU-based Encoder-Decoder Architecture with Bahdanau Attention Mechanism for Machine Translation from German to English.

Generate captions from images

Master Project on Image Captioning using Supervised Deep Learning Methods

This project is a Neural Machine Translation (NMT) system that translates French sentences into English using a Sequence-to-Sequence (Seq2Seq) architecture with Attention Mechanism and Beam Search Decoding. The model is built using TensorFlow and deployed through a FastAPI backend with a HTML, CSS, and JavaScript frontend.

Bangla Conversational Chatbot using Bidirectional LSTM with Attention Mechanism

A language translator based on a very simple NLP Transformer model, backed by encoder, decoder and a Bahdanau Attention Layer in between, implemented on TensorFlow.

Clean PyTorch reimplementation of Bahdanau attention NMT (NeurIPS 2014) with alignment visualizations.

s-atmech is an independent Open Source, Deep Learning python library which implements attention mechanism as a RNN(Recurrent Neural Network) Layer as Encoder-Decoder system. (only supports Bahdanau Attention right now).

基于 Seq2Seq、双向 GRU 与 Bahdanau 注意力机制实现的英法神经机器翻译项目。模型使用本地英法平行语料从零训练，不依赖在线翻译接口或预训练大语言模型，并提供完整的训练、评估、可视化、对比实验和桌面 Demo。

This repository contains an implementation of a neural text simplification model that combines sequence-to-sequence learning with reinforcement learning and lexical-semantic loss. The model aims to simplify complex text while maintaining meaning and grammatical correctness.

This repository contains TensorFlow/Keras models for implementing an Encoder-Decoder architecture for sequence-to-sequence tasks. It includes components such as Encoder, Decoder, Embedding Layer, LSTM Layer, Attention Mechanism, and more.

NetGuard is a production-grade Network Intrusion Detection System (NIDS) designed to classify sophisticated cyber threats across 15 distinct attack vectors. By treating network traffic as both a continuous 1D spatial signal and a discrete temporal sequence, NetGuard bypasses the limitations of single-modality intrusion detection.

Implemented an Encoder-Decoder model in TensorFlow, where ResNet-50 extracts features from the VizWiz-Captions image dataset and a GRU with Bahdanau attention generates captions.