This deep learning course provides a comprehensive introduction to attention mechanisms and transformer models the foundation of modern GenAI systems. Begin by exploring the shift from traditional neural networks to attention-based architectures. Understand how additive, multiplicative, and self-attention improve model accuracy in NLP and vision tasks. Dive into the mechanics of self-attention and how it powers models like GPT and BERT. Progress to mastering multi-head attention and transformer components, and explore their role in advanced text and image generation. Gain real-world insights through demos featuring GPT, DALL·E, LLaMa, and BERT.
Attention Mechanisms and Transformer Models Course
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
4 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
What you'll learn
Apply self-attention and multi-head attention in deep learning models
Understand transformer architecture and its key components
Explore the role of attention in powering models like GPT and BERT
Analyze real-world GenAI applications in NLP and image generation
Skills you'll gain
Details to know
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Recently updated!
June 2025
Assessments
7 assignments
Taught in English
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There are 2 modules in this course
Explore the power of attention mechanisms in modern deep learning. Compare traditional neural architectures with attention-based models to see how additive, multiplicative, and self-attention boost accuracy in NLP and vision tasks. Grasp the core math and flow of self-attention, the engine behind Transformer giants like GPT and BERT and build a solid base for advanced AI development.
What's included
10 videos1 reading3 assignments
Master multi-head attention and transformer models in this advanced module. Learn how multi-head attention improves context understanding and powers leading transformer architectures. Explore transformer components, text and image generation workflows, and real-world use cases with models like GPT, BERT, LLaMa, and DALL·E. Ideal for building GenAI-powered applications.
What's included
11 videos4 assignments
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Frequently asked questions
The attention mechanism allows transformer models to focus on relevant parts of input sequences, weighing relationships between tokens to improve context understanding and accuracy in tasks like translation or text generation.
Yes, ChatGPT is built on the transformer architecture, specifically using a variant of the GPT (Generative Pre-trained Transformer) model, which enables it to generate human-like responses.
The Vision Transformer (ViT) applies self-attention to image patches instead of pixels, enabling the model to capture spatial relationships and global context for accurate image classification and understanding.
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