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

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

Noise removal from images using Convolutional autoencoder

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

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

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

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.

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

Nougat is a Meta AI's revolutionary OCR model designed to transcribe scientific PDFs into an easy-to-use Markdown format.

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

Encoder-Decoder for Face Completion based on Gated Convolution

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

HTSM Masterwork

Source Code Generation Based On User Intention Using LSTM Networks

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