The tutorial notebooks in this repository are aimed to help beginners program basic recurrent neural networks(RNNs) for textual problems in tensorflow-2.

Prerequisite

• Understanding of basic textual processing methods, i.e familiarity with tokenization, etc.
• Functioning of basic RNN, GRU, LSTM cells
• Fundamentals of RNN based Encoder-Decoder Architectures
• Attention mechanism, mainly Bahdanau Attention and Luong Attention
• A basic idea of beam search algorithm.

Contents

1. utils directory contains helper classes and functions.

   • utils/dataset.py contains NMTDataset class which creates training and validation tf.data.Dataset splits and also returns input and target side tokenizers (tf.keras.preprocessing.text.Tokenizer). The working of utils/dataset.py have been explained in first notebook on text-processing notebook
   • utils/attention.py contains BahdanauAttention and LuongAttention class. These attention mechanisms have also been explained in [encoder-decoder with attention notebooks](lesson 4 and lesson 5)

2. tutorial-notebooks directory contains all the jupyter notebooks.

   • Notebook-1 (text-processing): explains how to use tf.keras.preprocessing module to preprocess textual corpus and prepare tf.data.Dataset objects.
   • Notebook-2 (embedding-and-classification): Fundamentals of a many-to-one recurrent neural network and how to program it in tensorflow-2.0
   • Notebook-3 (encoder-decoder): We build a encoder-decoder architecture with tf.keras.layers.GRU as the base recurrent layer in both encoder and decoder.
   • Notebook-4 (Bahdanau Attention): We describe the Bahdanau Attention mechanism and how it is incorporated in an encoder-decoder architecture.
   • Notebook-5 (Luong Attention): We describe Luong Attention and how is it incorporated in an encoder-decoder systm.
   • Notebook-6 (Stacked Encoder-Decoder): We build stacked(more than one layer) encoder with Bi-directional LSTM layers and stacked decoder with Unidirectional LSTM layers.
   • Notebook-7 (GoogleNMT): We program the architecture described in Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. The encoder and decoder stacks contain a total of 8 layers and residual connections from 3^rd layer onwards.
   • Notebook-8 (tf-addons): We learn how to use tf.addons.seq2seq module and how to use BasicDecoder and BeamSearchDecoder class for inference step.