Transformers for NLP and Computer Vision

Packt via Coursera Specialization

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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.

Syllabus

Course 1: Foundations of Transformer Architectures for Natural Language Processing
Course 2: Advanced Techniques and Interpretability in LLMs
Course 3: Transformers for Vision AI, Multimodal & Generative AI

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Delve into advanced transformer techniques, including generative AI, fine-tuning, interpretability, and the critical role of tokenization. Learn how to leverage and interpret large language models for a variety of sophisticated NLP tasks. This course covers the next level of transformer applications, focusing on generative AI with models like ChatGPT, advanced fine-tuning strategies, and the interpretability of model outputs. Learners will explore how tokenization shapes model performance, how embeddings can be used for search and transfer learning, and how to apply transformers to tasks such as semantic role labeling and summarization. By completing this course, you will be able to implement, fine-tune, and interpret large language models for complex NLP challenges. The course combines in-depth conceptual discussions with practical demonstrations, guiding learners through the intricacies of advanced transformer techniques and interpretability tools. Each topic is presented with clarity to ensure learners can apply these methods confidently. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Transformers for Natural Language Processing and Computer Vision, by Denis Rothman. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
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Explore the core principles and foundational architectures of transformers, focusing on their revolutionary impact on natural language processing. Gain a deep understanding of how transformer models work and the tasks they enable. This course introduces the fundamental concepts behind transformer models, tracing their evolution and examining their architecture in detail. Learners will discover how transformers have transformed natural language processing, from basic input representations to advanced tasks such as reading comprehension and translation. By the end of the course, you will be equipped to understand and evaluate transformer-based models and their applications in NLP. Through a blend of clear explanations, real-world examples, and guided explorations, this course builds your understanding of transformer models step by step. You will progress from foundational concepts to practical applications, ensuring a solid grasp of both theory and practice. This course is part one of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Transformers for Natural Language Processing and Computer Vision, by Denis Rothman. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.
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Discover how transformer architectures are revolutionizing computer vision and multimodal AI, and explore the latest advancements toward general artificial intelligence. Learn to apply transformers to image, video, and cross-modal tasks. This course explores the expanding frontier of transformer models beyond natural language, focusing on their applications in computer vision, multimodal AI, and generative ideation. Learners will investigate vision transformers, text-to-image and text-to-video generation, and the integration of multiple AI models for advanced tasks. The course also addresses risk mitigation in large models and looks ahead to the future of AI with functional AGI and creative generative systems. By the end, you will understand how to harness transformers for cutting-edge vision and multimodal applications. With a focus on emerging trends and practical implementations, this course guides learners through the latest research and real-world use cases in vision and multimodal AI. Concepts are introduced progressively, enabling learners to build expertise in applying transformers across diverse domains. This course is part three of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Transformers for Natural Language Processing and Computer Vision, by Denis Rothman. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.