attention-mechanism bahdanau-attention differentiable-neural-computer dynamic-memory-network end-to-end-memory-networks keras memory-networks neural-machine-translation nmt pytorch question-answering recurrent-entity-networks tensorflow

Chapter 9 - Attention and Memory Augmented Networks

In this chapter, we include two case studies. The first is the use of attention-based architectures for the task of neural machine translation. The second is the application of memory-augmented networks to the task of question answering.

Neural Machine Translation

Memory Augmented Networks

Book Reference

More information can be found at: Deep Learning for NLP and Speech Recognition by Springer

About

Chapter 9: Attention and Memory Augmented Networks

attention-mechanism bahdanau-attention differentiable-neural-computer dynamic-memory-network end-to-end-memory-networks keras memory-networks neural-machine-translation nmt pytorch question-answering recurrent-entity-networks tensorflow

Languages

Language:Jupyter Notebook 98.5%Language:Dockerfile 1.5%