Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.

High Quality Monocular Depth Estimation via Transfer Learning

Pytorch implemention of Deep CNN Encoder + LSTM Decoder with Attention for Image to Latex

Learning cell communication from spatial graphs of cells

A deep generative model to predict aircraft actual trajectories using high dimensional weather data

Invariant representation learning from imaging and spectral data

Noise removal from images using Convolutional autoencoder

Design and build a chatbot using data from the Cornell Movie Dialogues corpus, using Keras

Source code of: "Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models".

An Implementation of Encoder-Decoder model with global attention mechanism.

PyTorch tutorial for using RNN and Encoder-Decoder RNN for time series forecasting

Its a social networking chat-bot trained on Reddit dataset . It supports open bounded queries developed on the concept of Neural Machine Translation. Beware of its being sarcastic just like its creator 😝 BDW it uses Pytorch framework and Python3.

Encoder-Decoder for Face Completion based on Gated Convolution

This is an implementation of the paper "Show and Tell: A Neural Image Caption Generator".

📺 An Encoder-Decoder Model for Sequence-to-Sequence learning: Video to Text

Source Code Generation Based On User Intention Using LSTM Networks

This is the sequential Encoder-Decoder implementation of Neural Machine Translation using Keras

[Deep Learning] An end-to-end deep neural network that converts screenshots to Bootstrap (HTML/CSS) code

Encoder-Decoder model for Semantic Role Labeling

Using a CNN-LSTM hybrid network to generate captions for images