Pytorch Seq2Seq Tutorial for Machine Translation

Build a Sequence to Sequence (Seq2Seq) model from scratch for machine translation in this comprehensive 51-minute tutorial. Focus on translating German to English sentences using the Multi30k dataset. Learn about data processing with Torchtext, implementing the encoder and decoder, assembling the Seq2Seq model, setting up network training, troubleshooting errors, and evaluating model performance. Gain insights into the intricacies of Seq2Seq implementation, accompanied by detailed explanations and thoughts on the process. Explore additional resources for further learning, including GitHub repositories, academic papers, and PyTorch tutorials. Conclude with an evaluation of the model's performance using the BLEU score metric.

- Introduction
- Imports
- Data processing using Torchtext
- Implementation of Encoder
- Implementation of Decoder
- Putting it togethor to Seq2Seq
- Setting up training of the network
- Fixing Errors
- Evaluation of the model
- Ending and Bleu score result