Transformers and Attention Mechanisms
( 30 Modules )

Module #1
Introduction to Transformers
Overview of the Transformer architecture, its history, and applications

Module #2
Self-Attention Mechanism
In-depth explanation of the self-attention mechanism, including its mathematics and implementation

Module #3
Multi-Head Attention
Understanding multi-head attention, its benefits, and how it enhances representation learning

Module #4
Encoder-Decoder Architecture
Exploring the encoder-decoder architecture, its components, and how it is used in sequence-to-sequence tasks

Module #5
Positional Encoding
Understanding positional encoding, its importance, and how it is used in Transformers

Module #6
Transformer Applications
Overview of various applications of Transformers, including machine translation, text classification, and more

Module #7
Introduction to Attention Mechanisms
General overview of attention mechanisms, their types, and their applications

Module #8
Scaled Dot-Product Attention
In-depth explanation of scaled dot-product attention, its mathematics, and implementation

Module #9
Multi-Layer Perceptron (MLP) Attention
Understanding MLP attention, its benefits, and how it is used in various NLP tasks

Module #10
Hierarchical Attention
Exploring hierarchical attention, its components, and how it is used in document classification and other tasks

Module #11
Global Attention
Understanding global attention, its benefits, and how it is used in various NLP tasks

Module #12
Local Attention
Exploring local attention, its benefits, and how it is used in various NLP tasks

Module #13
Implementing Transformers with PyTorch
Hands-on implementation of Transformers using PyTorch, including coding exercises and projects

Module #14
Implementing Attention Mechanisms with TensorFlow
Hands-on implementation of attention mechanisms using TensorFlow, including coding exercises and projects

Module #15
Transformer Variants
Exploring various Transformer variants, including BERT, RoBERTa, and XLNet

Module #16
Attention Mechanisms in Computer Vision
Understanding attention mechanisms in computer vision, including applications and implementation

Module #17
Transformer-Based Models for NLP Tasks
Exploring various Transformer-based models for NLP tasks, including language modeling and text generation

Module #18
Fine-Tuning Pre-Trained Transformers
Understanding how to fine-tune pre-trained Transformers for specific NLP tasks

Module #19
Transformer Interpretability
Exploring techniques for interpreting and visualizing Transformer models

Module #20
Transformer Optimization Techniques
Understanding various optimization techniques for training Transformers, including batch optimization and parallelization

Module #21
Transformer Evaluation Metrics
Exploring evaluation metrics for Transformers, including BLEU score, ROUGE score, and more

Module #22
Real-World Applications of Transformers
Case studies and examples of real-world applications of Transformers, including chatbots, language translation, and more

Module #23
Challenges and Limitations of Transformers
Discussing challenges and limitations of Transformers, including overfitting, computational complexity, and more

Module #24
Future Directions and Research in Transformers
Exploring current research and future directions in Transformers, including new architectures and applications

Module #25
Project Development:Building a Transformer-Based Model
Hands-on project development, where students build and train a Transformer-based model for a specific NLP task

Module #26
Project Development:Implementing Attention Mechanisms
Hands-on project development, where students implement attention mechanisms for a specific NLP task

Module #27
Project Development:Fine-Tuning Pre-Trained Transformers
Hands-on project development, where students fine-tune pre-trained Transformers for a specific NLP task

Module #28
Project Development:Evaluating and Visualizing Transformers
Hands-on project development, where students evaluate and visualize Transformer models for a specific NLP task

Module #29
Case Studies and Group Discussions
Group discussions and case studies on real-world applications of Transformers and attention mechanisms

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Transformers and Attention Mechanisms career

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Transformers and Attention Mechanisms ( 30 Modules )

Transformers and Attention Mechanisms
( 30 Modules )