Generative AI NLP Specialization | Level 2

Announcing the Generative AI NLP Specialization | Level 2 Event!

Prepare to embark on an illuminating journey into the realm of advanced AI models at our upcoming event. Immerse yourself in the intricacies of Large Language Models (LLMs) as we delve into their architecture and real-world applications, shaping you into a master of modern language understanding and generation.

Whether you're an AI veteran or a curious newcomer, this event is tailored to all. Delve deep into the core principles and practical applications of transformative AI models, unlocking the potential to navigate the evolving landscape of language technology.

Event Highlights:

• In-depth exploration of LLM architecture and principles
• Techniques for evaluating LLM performance
• Insights into cutting-edge language understanding and generation

By the event's end, you'll walk away with:

• Comprehensive grasp of Large Language Models and their evaluation methods
• Practical skills to apply LLMs to your projects

Who Should Attend:

• Developers and Software Engineers eager to master Generative AI
• AI Enthusiasts and Professionals with a hunger for innovation
• Data Scientists and ML Practitioners looking to elevate their skills

Prerequisite

Completion of Generative AI NLP Specialization | Level 1 course or equivalent knowledge.

Join us for an event that promises to enhance your AI expertise and open doors to a world of limitless language possibilities. Limited spots available. Secure yours now and unlock the power of Generative AI!

Topics Covered

Module 1: Large Language Models (LLMs) Fundamentals

• Understanding Language Models
• What are Language Models?
• Importance of language understanding in AI.
• Examples               
  • Use cases and tasks of LLMs
  • LLM Example Architecture
  • Pretraining Large Language Models
  • Data collection, tokenization, masked language modeling
  • Transfer Learning and Fine-Tuning
  • Adapting pretrained models to specific tasks
  • Case Study: LLM Industry Use Case

      

Model 2: Evaluating LLMs

• Evaluating LLMs Significance and impact of Evaluation on natural language understanding and generation tasks
• Various Evaluation Metrics used to assess the quality and performance of LLMs
• Human Evaluation in assessing LLMs
• Intrinsic & Extrinsic Evaluation
• Dataset Quality and Bias
• Interpretability and Explainability
• Robustness and Generalization
• Fairness and Bias Evaluation

Module 3: Fine Tuning Basics

• Background and concept
• Curse of dimensionality
• Graphical models (Bayesian networks)
• Comparison of generative and discriminative models

Module 4: Hands On Lab

• Lab: Fine Tuning a LLMs for specific tasks