Description
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This course offers a comprehensive introduction to large language models (LLMs) and the challenges businesses face in adopting them. You’ll first explore the fundamentals of LLMs, their architecture, and core prompt engineering principles. Next, you’ll learn how to customize LLMs through fine-tuning, training from scratch, and in-context learning. The course will also delve into enterprise-specific challenges, including complex datasets, legal risks, and the costs associated with LLM adoption. By the end of the course, you’ll have the knowledge to interact with, optimize, and deploy LLMs in real-world applications.
What you'll learn
- Understand the basic structure and capabilities of large language models (LLMs).
- Learn how to customize LLMs by training from scratch and fine-tuning pre-existing models.
- Discover the benefits of in-context learning and its applications.
- Understand cost considerations and the complexities of integrating LLMs into businesses.
- Recognize challenges and risks, including hallucinations and prompt injection.
- Optimize prompt reliability while addressing ethical, privacy, and security concerns in business.
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