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Starts 24 June 2025 11:14

Ends 24 June 2025

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A Smarter Way to Fine-Tune LLMs - Unlock Deeper Reasoning

Join our session to explore a smarter way to fine-tune Large Language Models (LLMs) and unlock their deeper reasoning abilities. This approach improves generalization techniques derived from in-context learning, backed by cutting-edge research from Google DeepMind. Elevate your understanding and application of artificial intelligence with insig.
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Overview

Join our session to explore a smarter way to fine-tune Large Language Models (LLMs) and unlock their deeper reasoning abilities. This approach improves generalization techniques derived from in-context learning, backed by cutting-edge research from Google DeepMind.

Elevate your understanding and application of artificial intelligence with insights from experts in the field.

Whether you're delving into Artificial Intelligence or Computer Science, this course offers valuable knowledge and skills that are essential for advancing in these dynamic fields. Discover how LLM fine-tuning can revolutionize AI capabilities, particularly in enhancing reasoning and learning processes.

Hosted on YouTube, our platform provides a comprehensive and accessible way to learn and integrate these advanced AI concepts into your work or studies.

Don’t miss out on this opportunity to advance your expertise in artificial intelligence.

Syllabus

  • Introduction to Large Language Models (LLMs)
  • Overview of LLM architecture
    Current applications of LLMs
    Challenges in reasoning and generalization
  • Understanding Fine-Tuning
  • Basics of model fine-tuning
    Differences between pre-training and fine-tuning
    Limitations of conventional fine-tuning techniques
  • Advanced Fine-Tuning Techniques
  • Meta-learning for LLMs
    Prompt engineering and its impact on reasoning
    Zero-shot and few-shot learning approaches
  • Enhancing In-Context Learning
  • Concept of in-context learning
    Methods to improve contextual understanding
    Case studies on effective in-context learning
  • Improving Reasoning Capabilities
  • Mechanisms of reasoning in LLMs
    Techniques to enhance logical reasoning
    Evaluating reasoning improvements
  • Research Insights from Google DeepMind
  • Overview of key research findings
    Strategies for smarter fine-tuning
    Implications of DeepMind's research on future LLM development
  • Practical Applications and Case Studies
  • Real-world applications of improved LLM reasoning
    Case studies showcasing enhanced model performance
    Ethical considerations in deploying reasoning-optimized LLMs
  • Tools and Frameworks for LLM Fine-Tuning
  • Introduction to popular tools and libraries
    Setting up a fine-tuning environment
    Hands-on session: Fine-tuning a basic LLM for reasoning tasks
  • Evaluation and Metrics
  • Metrics for assessing reasoning and generalization
    Benchmark datasets for LLM evaluation
    Continuous monitoring and evaluation strategies
  • Future Directions in LLM Development
  • Emerging trends in LLM research
    The role of LLMs in advancing AI capabilities
    Potential challenges and areas for improvement
  • Course Conclusion
  • Summary of key learnings
    Interactive Q&A session
    Recommended resources for further study

Subjects

Computer Science