Pytorch Seq2Seq Tutorial for Machine Translation скачать в хорошем качестве

Pytorch Seq2Seq Tutorial for Machine Translation

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/interme... 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. ❤️ Support the channel ❤️    / @aladdinpersson   Paid Courses I recommend for learning (affiliate links, no extra cost for you): ⭐ Machine Learning Specialization https://bit.ly/3hjTBBt ⭐ Deep Learning Specialization https://bit.ly/3YcUkoI 📘 MLOps Specialization http://bit.ly/3wibaWy 📘 GAN Specialization https://bit.ly/3FmnZDl 📘 NLP Specialization http://bit.ly/3GXoQuP ✨ Free Resources that are great: NLP: https://web.stanford.edu/class/cs224n/ CV: http://cs231n.stanford.edu/ Deployment: https://fullstackdeeplearning.com/ FastAI: https://www.fast.ai/ 💻 My Deep Learning Setup and Recording Setup: https://www.amazon.com/shop/aladdinpe... GitHub Repository: https://github.com/aladdinpersson/Mac... ✅ One-Time Donations: Paypal: https://bit.ly/3buoRYH ▶️ You Can Connect with me on: Twitter -   / aladdinpersson   LinkedIn -   / aladdin-persson-a95384153   Github - https://github.com/aladdinpersson 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