Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
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Updated
Jan 20, 2024 - Jupyter Notebook
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
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
Build a deep neural network with keras that functions as part of an end-to-end machine translation pipeline
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
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.
Interpreting natural language navigational instructions
Encoder-Decoder model for Semantic Role Labeling
Pytorch implementation of FTNet for Semantic Segmentation on SODA, SCUT Seg, and MFN Datasets
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