Code 7 Landmark NLP Papers in PyTorch (Full NMT Course)

Curriculum for the course Code 7 Landmark NLP Papers in PyTorch (Full NMT Course)

This course is a comprehensive journey through the evolution of sequence models and neural machine translation (NMT). It blends historical breakthroughs, architectural innovations, mathematical insights, and hands-on PyTorch replications of landmark papers that shaped modern NLP and AI. The course features: - A detailed narrative tracing the history and breakthroughs of RNNs, LSTMs, GRUs, Seq2Seq, Attention, GNMT, and Multilingual NMT. - Replications of 7 landmark NMT papers in PyTorch, so learners can code along and rebuild history step by step. - Explanations of the math behind RNNs, LSTMs, GRUs, and Transformers. - Conceptual clarity with architectural comparisons, visual explanations, and interactive demos like the Transformer Playground. 🌐 Atlas Page: https://programming-ocean.com/knowledge-hub/neural-machine-translation-atlas.php 💻 Code Source on Github: https://github.com/MOHAMMEDFAHD/Pytorch-Collections/tree/main/Neural-Machine-Translation ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp ⭐️ Chapters ⭐️ – 0:01:06 Welcome – 0:04:27 Intro to Atlas – 0:09:25 Evolution of RNN – 0:15:08 Evolution of Machine Translation – 0:26:56 Machine Translation Techniques – 0:34:28 Long Short-Term Memory (Overview) – 0:52:36 Learning Phrase Representation using RNN (Encoder–Decoder for SMT) – 1:00:46 Learning Phrase Representation (PyTorch Lab – Replicating Cho et al., 2014) – 1:23:45 Seq2Seq Learning with Neural Networks – 1:45:06 Seq2Seq (PyTorch Lab – Replicating Sutskever et al., 2014) – 2:01:45 NMT by Jointly Learning to Align (Bahdanau et al., 2015) – 2:32:36 NMT by Jointly Learning to Align & Translate (PyTorch Lab – Replicating Bahdanau et al., 2015) – 2:42:45 On Using Very Large Target Vocabulary – 3:03:45 Large Vocabulary NMT (PyTorch Lab – Replicating Jean et al., 2015) – 3:24:56 Effective Approaches to Attention (Luong et al., 2015) – 3:44:06 Attention Approaches (PyTorch Lab – Replicating Luong et al., 2015) – 4:03:17 Long Short-Term Memory Network (Deep Explanation) – 4:28:13 Attention Is All You Need (Vaswani et al., 2017) – 4:47:46 Google Neural Machine Translation System (GNMT – Wu et al., 2016) – 5:12:38 GNMT (PyTorch Lab – Replicating Wu et al., 2016) – 5:29:46 Google’s Multilingual NMT (Johnson et al., 2017) – 6:00:46 Multilingual NMT (PyTorch Lab – Replicating Johnson et al., 2017) – 6:15:49 Transformer vs GPT vs BERT Architectures – 6:36:38 Transformer Playground (Tool Demo) – 6:38:31 Seq2Seq Idea from Google Translate Tool – 6:49:31 RNN, LSTM, GRU Architectures (Comparisons) – 7:01:08 LSTM & GRU Equations 🎉 Thanks to our Champion and Sponsor supporters: 👾 Drake Milly 👾 Ulises Moralez 👾 Goddard Tan 👾 David MG 👾 Matthew Springman 👾 Claudio 👾 Oscar R. 👾 jedi-or-sith 👾 Nattira Maneerat 👾 Justin Hual -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

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