From the course: Deep Learning: Getting Started
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Foundation models
From the course: Deep Learning: Getting Started
Foundation models
Foundation models are designed to be broadly useful instead of being built for only one task. They learn from large and diverse datasets, which allows them to support many different kinds of AI applications. For example, the same base model may be used to summarize documents, answer questions, translate text, classify content, generate code, or create images. This is one of the major reasons foundation models became very important in generative AI. Earlier AI systems were often built for one specific task. For example, one model might classify images while another model might translate text. Foundation models changed this pattern by giving us a reusable starting point. Instead of training a new model from scratch, we can adapt an existing model for different use cases. This reduces the time, data, and compute required for building AI applications. It also makes advanced AI capabilities more accessible to developers and organizations. Foundation models also speed up experimentation…
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