Attention Mechanisms and Transformer Models Course
About this course
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. To be successful in this course, you should have a basic understanding of neural networks, machine learning concepts, and Python programming. By the end of this course, you’ll be able to: - Explain how attention mechanisms enhance deep learning models - Implement and apply self-attention and multi-head attention - Understand transformer architecture and real-world use cases - Analyze leading GenAI models across NLP and image generation Ideal for AI developers, ML engineers, and data scientists.
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What you'll learn
- Explain how attention mechanisms enhance deep learning models
- Implement and apply self-attention and multi-head attention
- Understand transformer architecture and real-world use cases
- Analyze leading GenAI models across NLP and image generation
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