A practical introduction to large language models : uses and strategies for real-world AI applications.

Format:

Video

Contributor:

Reganti, Aishwarya Naresh, instructor.

O'Reilly (Firm), publisher.

Language:

English

Subjects (All):

Natural language generation (Computer science).

Artificial intelligence--Computer programs.

Artificial intelligence.

Natural language processing (Computer science).

Physical Description:

1 online resource (1 video file (1 hr., 58 min.)) : sound, color.

Edition:

[First edition].

Place of Publication:

[Sebastopol, California] : O'Reilly Media, Inc., [2025]

Summary:

Large language models (LLMs) are rapidly evolving to automate tasks with their advanced understanding and generation abilities. This momentum is fueled by an increasing adoption among both companies and individuals. With their increasing usage and transformative effects across various domains, knowing how they work and maximizing their effectiveness has become a highly sought-after skill. Although there are various resources available online, many lack organization and/or contain excessive technical details, making them overwhelming to understand. This course provides participants with fundamental skills to excel with LLMs in practical contexts. It provides a comprehensive understanding of their usage while prioritizing real-world applications over technical groundwork. Learners will explore the three key paradigms of LLM utilization: in-context usage, Retrieval-Augmented Generation (RAG), and fine-tuning. For each paradigm, we'll cover basics, commonly used techniques, and real-world examples of their optimal application. Additionally, we'll conduct an in-depth analysis of when to employ each paradigm based on various external constraints. Mastering these basics will empower participants to confidently apply LLMs across various practical scenarios, selecting the most suitable paradigm and technique for each. Additionally, this course provides a strong groundwork for diving into advanced LLM concepts in the future.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1499860465

Publisher Number:

0642572041847