Introduction to Attention-Based Neural Networks

Learn what attention-based models are, how they work, and what they can do for recurrent neural networks.

Introduction

• Prerequisites
• What are attention-based models?
• Attention in language generation and translation models

1. Recurrent Neural Networks to Learn Sequential Data

• Feed forward networks and their limitations
• Recurrent neural networks for sequential data
• The need for long memory cells
• LSTM and GRU cells
• Types of RRNNS

2. Encoder-Decoder Networks for Language Models

• Language generation models
• Sequence to sequence models for language translation

3. Attention-Based Neural Networks

• The role of attention in sequence to sequence models
• Attention mechanism in sequence to sequence models
• Alignment weights in attention models
• Bahdanau attention
• Attention models for image captioning
• Encoder decoder structure for image captioning

4. Image Captioning Model without Attention

• Setting up Colab and Google Drive
• Loading in the Flickr8k dataset
• Constructing the vocabulary
• Setting up the dataset class
• Implementing utility functions for training data
• Building the encoder CNN
• Building the decoder RNN
• Setting up the sequence to sequence model
• Training the image captioning model

5. Image Captioning Model Using Attention

• Loading the dataset and setting up utility functions
• The encoder CNN generating unrolled feature maps
• Implementing Bahdanau attention
• The decoder RNN using attention
• Generating captions using attention
• Training the attention-based image captioning model
• Visualizing the model's attention

Conclusion

• Summary and next steps