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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:
💻 Code Source on Github:
❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning:
⭐️ 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
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2025/12/10
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