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10 Modules / ~100 pages
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~25 Modules / ~400 pages

Transformers and Attention Mechanisms in NLP
( 30 Modules )

Module #1
Introduction to Transformers
Overview of transformer architecture and its applications in NLP
Module #2
History of NLP Models
From traditional RNNs to Transformers:A brief history of NLP model evolution
Module #3
Self-Attention Mechanism
Understanding self-attention mechanism and its importance in Transformers
Module #4
Multi-Head Attention
Implementing multi-head attention in Transformer models
Module #5
Encoder-Decoder Architecture
Understanding the encoder-decoder architecture in Transformers
Module #6
Transformer Model Architecture
In-depth analysis of the Transformer model architecture
Module #7
Positional Encoding
Understanding positional encoding in Transformers
Module #8
Attention Weights and Visualization
Visualizing attention weights in Transformer models
Module #9
BERT and Its Variants
Overview of BERT, RoBERTa, and other variants of pre-trained language models
Module #10
Fine-Tuning Pre-Trained Models
Practical approaches to fine-tuning pre-trained language models
Module #11
Attention in Computer Vision
Applying attention mechanisms to computer vision tasks
Module #12
Transformer-based Models for NLP Tasks
Applications of Transformers in sentiment analysis, machine translation, and more
Module #13
Efficient Transformer Architectures
Introducing efficient Transformer architectures for faster inference
Module #14
Long-Range Dependencies and Efficient Attention
Addressing long-range dependencies and efficient attention mechanisms
Module #15
Hierarchical and tree-based Transformers
Transformers for hierarchical and tree-based data structures
Module #16
Graph-based Transformers
Applying Transformers to graph-structured data
Module #17
Explainability and Interpretability in Transformers
Techniques for explaining and interpreting Transformer models
Module #18
Common Challenges and Solutions
Addressing common challenges in training and deploying Transformer models
Module #19
Case Studies:Real-World Applications
Real-world applications and case studies of Transformers in NLP
Module #20
Advanced Topics in Transformers
Exploring advanced topics in Transformers, including XLNet and more
Module #21
Comparing Transformers with Other Models
Comparing Transformers with other NLP models, including RNNs and CNNs
Module #22
Future Directions in Transformer Research
Research directions and future applications of Transformers in NLP
Module #23
Practical Exercise 1:Implementing a Basic Transformer
Hands-on exercise:Implementing a basic Transformer model
Module #24
Practical Exercise 2:Fine-Tuning a Pre-Trained Model
Hands-on exercise:Fine-tuning a pre-trained language model
Module #25
Practical Exercise 3:Applying Transformers to a Real-World Task
Hands-on exercise:Applying Transformers to a real-world NLP task
Module #26
Practical Exercise 4:Visualizing Attention Weights
Hands-on exercise:Visualizing attention weights in a Transformer model
Module #27
Practical Exercise 5:Implementing Efficient Transformer Architectures
Hands-on exercise:Implementing efficient Transformer architectures
Module #28
Practical Exercise 6:Exploring Explainability Techniques
Hands-on exercise:Exploring explainability techniques for Transformer models
Module #29
Final Project:Developing a Transformer-based NLP Model
Final project:Developing a Transformer-based NLP model for a real-world task
Module #30
Course Wrap-Up & Conclusion
Planning next steps in Transformers and Attention Mechanisms in NLP career


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