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Introduction to the fundamentals of prompt learning within the context of foundation models including large language models and large visual models.

Exploration of the motivations behind prompt learning as a technique to address challenges in computer vision and natural language processing.

Addressing the fundamental issues with the traditional paradigm in machine learning and exploring prevalent trends in the field.

Delving into the transition from hard prompt techniques to more flexible soft prompting methods using large language models.

Explanation of how tasks in natural language processing and computer vision evolved over time and drove progress in the field of AI.

Discussion on the breakthrough in object recognition achieved through deep convolutional networks like Alexnet.

Transition in machine learning paradigms from learning from scratch to pre-train then fine-tune paradigm impacting natural language processing development.

Discussion on the emergence of powerful language models like GPT models and their descendants due to training on extensive datasets.

Explanation of how foundation models benefit specific tasks by fine-tuning them on downstream datasets for improved performance.

Comparison of computational resources required for fine-tuning foundation models versus prompt tuning and the advantages of prompt tuning with fewer parameters.

Details on prompting in natural language processing, including prompt templates, slots, and fine-tuning using prompt vectors.

Explanation of prompt tuning methods shifting from prompt engineering to prompt tuning, updating prompt vectors during training, and using soft prompts.

Discussion on multimodal foundation models in computer vision, image classification tasks, and the combination of visual and textual input for enhanced performance.