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

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

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

Learning cell communication from spatial graphs of cells

Invariant representation learning from imaging and spectral data

Noise removal from images using Convolutional autoencoder

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

Build a deep neural network with keras that functions as part of an end-to-end machine translation pipeline

Support material and source code for the model described in : "A Recurrent Encoder-Decoder Approach With Skip-Filtering Connections For Monaural Singing Voice Separation"

Chatbot implementation using Cornell Movie Dialog Dataset in PyTorch.The bot can converse with the user and can answer the questions asked though it doesn't pass the Turing Test

Encoder-Decoder for Face Completion based on Gated Convolution

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

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

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 Implementation of Encoder-Decoder model with global attention mechanism.

Explore diverse computer vision projects using Transfer Learning(TL), Convolutional Neural Networks (CNN), Autoencoder and more in this collaborative repository

Interpreting natural language navigational instructions

Encoder-Decoder model for Semantic Role Labeling