Summary: Sequence-to-Sequence Learning—NMT

Understand the fundamentals of sequence-to-sequence learning applied to neural machine translation. Learn about model components like embedding layers, encoders, decoders, and attention mechanisms. Discover how training is conducted using teacher forcing and how performance is evaluated using the BLEU metric. Gain insight into related applications such as chatbots and their evaluation with the Turing test.

We'll cover the following...

The machine translation problem
Sequence-to-sequence learning for chatbots

1.Introduction to Natural Language Processing

2.Understanding TensorFlow 2

3.Word2vec: Learning Word Embeddings

4. Advanced Word Vector Algorithms

5.Sentence Classification with Convolutional Neural Networks

6.Recurrent Neural Networks

7.Understanding Long Short-Term Memory Networks

8.Applications of LSTM: Generating Text

9.Sequence-to-Sequence Learning: Neural Machine Translation

10.Transformers

Project

11.Image Captioning with Transformers

12.Final Remarks

13.Appendix: Mathematical Foundations and Advanced TensorFlow

Mock Interview

Summary: Sequence-to-Sequence Learning—NMT

The machine translation problem