<commit_before># Neural Machine Translation (seq2seq) Tutorial

*Authors: <NAME>, <NAME>, <NAME> ([Google Research Blogpost](https://research.googleblog.com/2017/07/building-your-own-neural-machine.html))*

*This version of the tutorial requires [TensorFlow Nightly](https://github.com/tensorflow/tensorflow/#installation).
For using the stable TensorFlow versions, please consider other branches such as
[tf-1.2](https://github.com/tensorflow/nmt/tree/tf-1.2).*

*If make use of this codebase for your research, please cite
[this](#bibtex).*

- [Introduction](#introduction)
- [Basic](#basic)
   - [Background on Neural Machine Translation](#background-on-neural-machine-translation)
   - [Installing the Tutorial](#installing-the-tutorial)
   - [Training – *How to build our first NMT system*](#training--how-to-build-our-first-nmt-system)
      - [Embedding](#embedding)
      - [Encoder](#encoder)
      - [Decoder](#decoder)
      - [Loss](#loss)
      - [Gradient computation & optimization](#gradient-computation--optimization)
   - [Hands-on – *Let's train an NMT model*](#hands-on--lets-train-an-nmt-model)
   - [Inference – *How to generate translations*](#inference--how-to-generate-translations)
- [Intermediate](#intermediate)
   - [Background on the Attention Mechanism](#background-on-the-attention-mechanism)
   - [Attention Wrapper API](#attention-wrapper-api)
   - [Hands-on – *Building an attention-based NMT model*](#hands-on--building-an-attention-based-nmt-model)
- [Tips & Tricks](#tips--tricks)
   - [Building Training, Eval, and Inference Graphs](#building-training-eval-and-inference-graphs)
   - [Data Input Pipeline](#data-input-pipeline)
   - [Other details for better NMT models](#other-details-for-better-nmt-models)
      - [Bidirectional RNNs](#bidirectional-rnns)
      - [Beam search](#beam-search)
      - [Hyperparameters](#hyperparameters)<commit_msg>Add github link to README<commit_after># Neural Machine Translation (seq2seq) Tutorial

*Authors: <NAME>, <NAME>, <NAME> ([Google Research Blogpost](https://research.googleblog.com/2017/07/building-your-own-neural-machine.html), [Github](https://github.com/tensorflow/nmt))*

*This version of the tutorial requires [TensorFlow Nightly](https://github.com/tensorflow/tensorflow/#installation).
For using the stable TensorFlow versions, please consider other branches such as
[tf-1.2](https://github.com/tensorflow/nmt/tree/tf-1.2).*

*If make use of this codebase for your research, please cite
[this](#bibtex).*

- [Introduction](#introduction)
- [Basic](#basic)
   - [Background on Neural Machine Translation](#background-on-neural-machine-translation)
   - [Installing the Tutorial](#installing-the-tutorial)
   - [Training – *How to build our first NMT system*](#training--how-to-build-our-first-nmt-system)
      - [Embedding](#embedding)
      - [Encoder](#encoder)
      - [Decoder](#decoder)
      - [Loss](#loss)
      - [Gradient computation & optimization](#gradient-computation--optimization)
   - [Hands-on – *Let's train an NMT model*](#hands-on--lets-train-an-nmt-model)
   - [Inference – *How to generate translations*](#inference--how-to-generate-translations)
- [Intermediate](#intermediate)
   - [Background on the Attention Mechanism](#background-on-the-attention-mechanism)
   - [Attention Wrapper API](#attention-wrapper-api)
   - [Hands-on – *Building an attention-based NMT model*](#hands-on--building-an-attention-based-nmt-model)
- [Tips & Tricks](#tips--tricks)
   - [Building Training, Eval, and Inference Graphs](#building-training-eval-and-inference-graphs)
   - [Data Input Pipeline](#data-input-pipeline)
   - [Other details for better NMT models](#other-details-for-better-nmt-models)
      - [Bidirectional RNNs](#bidirectional-rnns)
      - [Beam search](#beam-search)
      - [Hyperparameters](#hyperparameters)
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