Generative Al NLP Specialization | Level 3
Dive Deeper into LLMs and it’s fine tuning techniques: Generative Al NLP Specialization.
Type
PaidSeptember 19 @ 9:00 am – 5:00 pm UTC+5:30
About the event
Join us for an exclusive event to elevate your Generative AI NLP expertise with advanced techniques. Explore the captivating realm of fine-tuning advanced Large Language Models (LLMs), to unlock their potential for real-world impact. This event is a unique opportunity to cultivate proficiency in refining LLMs and aligning them seamlessly with human values. Through structured exploration and hands-on exercises, you’ll uncover the intricacies of reinforcement learning, parameter-efficient fine-tuning, and effective model evaluation.
Key Outcomes and Benefits:
1. Expert-Level Skills: Develop mastery in advanced LLM fine-tuning techniques to create more precise and impactful models.
2. Ethical Alignment: Learn how to align LLMs with human values, ensuring ethical, responsible, and relevant applications.
3. Cutting-edge Insights: Gain insights into groundbreaking techniques like instruction fine-tuning, parameter efficiency, LoRA, and Soft Prompts.
4. Professional Advancement: Enhance your profile as a developer, software engineer, AI enthusiast, or data scientist with advanced Generative AI skills.
5. Network Building: Connect with a community of like-minded professionals, fostering collaborations and idea exchange.
Who Should Attend:
- Developers and software engineers seeking to elevate their Generative AI skills.
- AI enthusiasts and professionals committed to developing intelligent and innovative solutions.
- Data scientists and machine learning practitioners aiming to refine their capabilities in cutting-edge GenAI models.
Prerequisite:
Prior completion of Generative AI NLP Specialization | Level 2 or equivalent knowledge is recommended.
Join us to amplify your expertise in Generative AI. Secure your spot at “Generative Al NLP Specialization | Level 3” and unlock the power of finely-tuned language models.
- Introduction to Large Language Models
- Pre-training Large Language Models
- Computational Challenges in Training LLMs
- Scaling Laws and Compute-Optimal Models
- Fine-tuning Techniques
- Instruction Fine-tuning
- Fine-tuning on a Single Task
- Multi-task Instruction Fine-tuning
- Parameter Efficient Fine-tuning (PEFT)
- PEFT Techniques: LoRA and Soft Prompts
- Model Evaluation and Benchmarks
- Evaluating Fine-tuned Models
- Introduction to Benchmarks
- Introduction
- Overview of Fine-tuning Large Language Models
- Importance of Aligning Models with Human Values
- Reinforcement Learning from Human Feedback (RLHF)
- Introduction to RLHF
- Obtaining Feedback from Humans
- Developing a Reward Model for RLHF
- Fine-tuning with Reinforcement Learning
- Fine-tuning Process using RLHF
- Techniques for Optimizing RLHF Performance
- Optional Video: Proximal Policy Optimization
- Addressing Reward Hacking
- Scaling Human Feedback
- Challenges and Considerations
- Strategies for Collecting and Incorporating Large-scale Feedback
- Evaluation and Assessment
- Methods for Evaluating Fine-tuned Language Models
- Assessing Model Performance in Alignment with Human Values
- Lab: Transforming Human Interactions with AI ( RLHF)
