Build a Custom Translator with LSTMs in PyTorch
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Learn to build a custom language translator using Long Short-Term Memory (LSTM) networks in PyTorch. Explore how to preprocess text data, train LSTM models, and deploy translation systems for multilingual applications.
At a Glance
Build a translation system using PyTorch’s seq2seq models with LSTM units. In this project, you will set up an encoder-decoder architecture, train and evaluate the model on a large dataset, and generate translations, emphasizing practical NLP applications. Gain foundational skills in machine translation and explore advanced sequence-based tasks like text summarization and question-answering.
What you’ll learn
– Understand and implement the sequence-to-sequence (seq2seq) model architecture using PyTorch
– Preprocess text data effectively for machine translation tasks
– Set up and train an encoder-decoder architecture with LSTM units on a dataset, gaining insights into model training and optimization
– Evaluate the model using BLEU score
– Create a user interface with Gradio to generate translations
– Explore practical applications of NLP, enhancing your capability to complete various sequence-based tasks
What you’ll need
– Basic knowledge of Python programming
– Familiarity with PyTorch library, as it will be the primary framework used for model building
– Understanding of fundamental concepts in machine learning, especially neural networks
– Access to a modern web browser like Chrome, Edge, Firefox, Internet Explorer, or Safari, as the IBM Skills Network Labs environment is optimized for these
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