In this tutorial we build a Sequence to Sequence (Seq2Seq) model from scratch and apply it to machine translation on a dataset with German to English sentences, specifically the Multi30k dataset. There was a lot of things to go through and explain so the video is a bit longer than my normal videos, but I really felt I wanted to share my thoughts, explanations and the details of the implementation!

Resources I used and read to learn about Seq2Seq:
https://github.com/bentrevett
https://arxiv.org/abs/1409.3215
https://pytorch.org/tutorials/....intermediate/seq2seq

Comment on resources:
I think bentrevett on Github is awesome and I was heavily inspired in this video by his Seq2Seq Tutorials and I really recommend checking him out, he puts out a lot of great tutorials on his Github.

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

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OUTLINE:
0:00 - Introduction
1:27 - Imports
2:05 - Data processing using Torchtext
5:55 - Implementation of Encoder
11:02 - Implementation of Decoder
19:43 - Putting it togethor to Seq2Seq
27:57 - Setting up training of the network
41:03 - Fixing Errors
42:18 - Evaluation of the model
49:32 - Ending and Bleu score result

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