Transformers for NLP and Computer Vision
About this course
This specialization provides a comprehensive journey through the theory and application of transformer architectures in natural language processing and computer vision. Beginning with the foundational course, you will explore the core principles and structures of transformers, gaining insight into their transformative impact on NLP tasks such as reading comprehension and translation. The next stage delves into advanced techniques, including generative AI, fine-tuning, interpretability, and the pivotal role of tokenization. You will learn to leverage large language models for sophisticated NLP challenges, mastering methods for model adaptation and output analysis. The final course in this specialization expands your expertise to the cutting edge of AI, focusing on transformer applications in computer vision, multimodal AI, and generative systems. You will investigate vision transformers, text-to-image and text-to-video generation, and the integration of multiple AI models, while also considering risk mitigation and the future trajectory of general artificial intelligence. By progressing through these stages, you will develop the skills to understand, implement, and innovate with transformer models across diverse domains. This specialization is based on the book, Transformers for Natural Language Processing and Computer Vision, by Denis Rothman.
68/100
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- What the provider tells you
- 32/45
- Who stands behind it
- 20/35
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What you'll learn
- understand core principles of transformers
- apply transformers to natural language processing tasks
- implement advanced techniques like fine-tuning and generative AI
- explore applications of transformers in computer vision
- analyze outputs and adapt large language models
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