LinkedIn – Fine-Tuning for LLMs: from Beginner to Advanced 2024-9

Fine-Tuning course for LLMs: from Beginner to Advanced. This course focuses on recent developments in large language models such as FLAN-T5 and helps practitioners to effectively exploit these tools in the growing artificial intelligence landscape. Course instructor Axel Sirota will help you build a solid foundation in the fundamentals of LLMs and explore their architecture, evolution, and role in the current AI landscape. You will study query engineering and learn how to design effective queries to drive LLM outputs for specific tasks. You will then delve into learning transfer and fine-tuning PEFT using LoRA and discover how LLMs can be adapted and optimized for diverse NLP tasks. Each part of the course includes live clips, slides, and demos, as well as real-world challenges that cover query engineering, learning transfer, and fine-tuning techniques to improve FLAN-T5 capabilities. In addition, you will complete a final project that focuses on building an NLP solution focused on sentiment analysis, text summarization and question answering.

What you will learn:

• Deep understanding of the architecture and functionality of large language models
• Mastery of query engineering to drive LLM outputs
• Transfer learning and fine-tuning of models for specific tasks
• Build and implement custom LLM models for various NLP applications
• Developing a practical project to enhance learning skills

This course is suitable for people who:

• Seeking deep understanding of large language models
• They want to improve their natural language processing skills
• They are interested in developing applications based on artificial intelligence
• They want to learn the latest developments in artificial intelligence
• Data scientists, machine learning engineers and software developers looking to expand their knowledge

Details of Fine-Tuning course for LLMs: from Beginner to Advanced

• Publisher: LinkedIn
• Lecturer: Axel Sirota
• Training level: beginner to advanced
• Training duration: 3 hours and 25 minutes

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Sample video of the course

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