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​Building NMT from Scratch – PyTorch Replications of 7 Landmark Papers

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Learn about the complete neural machine translation journey. We just posted a course on the freeCodeCamp.org YouTube channel that is a comprehensive journey through the evolution of sequence models and neural machine translation (NMT). It blends hist… 

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Learn about the complete neural machine translation journey.

We just posted a course on the freeCodeCamp.org YouTube channel that 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.

Here are all the sections in the course:

  • Evolution of RNN

  • Evolution of Machine Translation

  • Machine Translation Techniques

  • Long Short-Term Memory (Overview)

  • Learning Phrase Representation using RNN (Encoder–Decoder for SMT)

  • Learning Phrase Representation (PyTorch Lab – Replicating Cho et al., 2014)

  • Seq2Seq Learning with Neural Networks

  • Seq2Seq (PyTorch Lab – Replicating Sutskever et al., 2014)

  • NMT by Jointly Learning to Align (Bahdanau et al., 2015)

  • NMT by Jointly Learning to Align & Translate (PyTorch Lab – Replicating Bahdanau et al., 2015)

  • On Using Very Large Target Vocabulary

  • Large Vocabulary NMT (PyTorch Lab – Replicating Jean et al., 2015)

  • Effective Approaches to Attention (Luong et al., 2015)

  • Attention Approaches (PyTorch Lab – Replicating Luong et al., 2015)

  • Long Short-Term Memory Network (Deep Explanation)

  • Attention Is All You Need (Vaswani et al., 2017)

  • Google Neural Machine Translation System (GNMT – Wu et al., 2016)

  • GNMT (PyTorch Lab – Replicating Wu et al., 2016)

  • Google’s Multilingual NMT (Johnson et al., 2017)

  • Multilingual NMT (PyTorch Lab – Replicating Johnson et al., 2017)

  • Transformer vs GPT vs BERT Architectures

  • Transformer Playground (Tool Demo)

  • Seq2Seq Idea from Google Translate Tool

  • RNN, LSTM, GRU Architectures (Comparisons)

  • LSTM & GRU Equations

Watch the full course on the freeCodeCamp.org YouTube channel (7-hour watch).