Generative Al NLP Specialization | Level 3

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.

Topics Covered

Module 1:  Deepdive into Fine-tuning LLMs

• 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

      

Module 2:  Reinforcement Learning and LLM-powered Applications

• 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)