Explore the fundamentals of machine translation in this comprehensive tutorial. Delve into various machine learning techniques, vectorization methods, and advanced concepts like Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Learn about sequence-to-sequence models, encoder-decoder architectures, and the teacher forcing mechanism. Apply your knowledge to a practical use case, creating a model that translates English text into French. Gain insights into statistical and neural machine translation models, and understand how to address challenges like gradient explosion. Perfect for those seeking to enhance their understanding of natural language processing and its applications in language translation.
Overview
Syllabus
Introduction.
Agenda.
What is Machine Translation?.
Statistical Machine Translation Model.
Neural Machine Translation Model.
NLP Recap with Deep Learning - Text Vectorisation.
NLP Recap with Deep Learning - RNN.
NLP Recap with Deep Learning - Exponential Gradient Problem.
NLP Recap with Deep Learning - LSTM.
NLP Recap with Deep Learning - GRU.
Sequence to Sequence Model.
Usecase.
Summary.
Taught by
Great Learning
Related Courses
Related articles
Reviews
4.5 rating, based on 2 Class Central reviews
-
it is very usefull couse to learn about RNN,LSTM,GRU and seq2seq model . create more video like this especially about LLM models
-
Nessma DiabGood intro to MT, I couldn't find the script for the use case, though, and I had to re-write from the scratch.
