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Building Machine Translation models for English-Hindi

In this project, we’ll learn to convert English text into Hindi using machine translation models and deep learning techniques.  We’ll start with a simple machine translation LSTM encoder-decoder model, then we’ll move to an attention-based encoder-decoder model, and we’ll use the Loung attention mechanism. After this, we’ll build the model using Bi-directional LSTM, and then we’ll train the transformer-based model with multi-head attention for translating English text into Hindi. 
Course
Materials

What will you Learn in the Project?

  1. Building machine translation model using LSTM 
  2. Building machine translation model using LSTM with an attention mechanism 
  3. Building machine translation model with Bi-directional LSTM Encoder-decoder 
  4. Building machine translation model with state-of-the-art transformer architecture

Tools & Technologies Used

  1. Pandas 
  2. Keras
  3. Tensorflow 
  4. Matplotlib 
  5. sklearn 
  6. Pandas 
  7. Nltk

Prerequisite

  1. Working knowledge of tools such as Tensorflow, Keras, etc. 
  2. Good theoretical understanding of concepts such as RNN, LSTM, Bi-directional LSTM, Encoder-decoder model, attention mechanism (such as loung attention), and deep learning transformer encoder-decoder model
  3. Having theoretical knowledge of how machine translation deep learning is a plus

Tasks Performed

Task-1: Create a base encoder-decoder machine translation model with LSTM

Task-2: Create a machine translation model with Luong-style attention

Task-3: Create a Bi-directional enc-dec model with an attention mechanism 

Task-4: Develop a transformer-based machine translation model

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Skills you will develop

Building machine translation model using LSTM

Building machine translation model using LSTM with an attention mechanism

Building machine translation model with Bi-directional LSTM Encoder-decoder

Building machine translation model with state-of-the-art transformer architecture

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