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Introduction to Attention-Based Neural Networks

Offered By: LinkedIn Learning

Tags

Long short-term memory (LSTM) Courses Image Captioning Courses Language Models Courses Sequence to Sequence Models Courses

Course Description

Overview

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

Syllabus

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

Taught by

Janani Ravi

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