Course Description

Elevate your tech and AI career with our cutting-edge Building AI Application with LLM training program. Discover the limitless potential of Building AI application and redefine your expertise in working with large language models.

Objectives

This program equips you with the skills to build AI Application with LLM and dive into real-world case studies that showcase with practical knowledge.

Audience

Looking to take your career in AI and technology to new heights? Our Building an application with LLM training program is designed to empower professionals of all backgrounds, whether you're a seasoned AI researcher, software engineer, data scientist professionals who want to learn and understand the techniques of finetuning, with basic knowledge of python and a knowledge with deep learning framework pyTorch.

Prerequisites

Anyone can attend the course.

Content

Module 0: Introduction to AI

• What is Artificial Intelligence
• Brief history of AI
• Importance and applications of AI  
• Building blocks of AI
• Type of AI  
• AI in Real-world Applications
• Healthcare applications
• Finance and trading
• Autonomous vehicles
• Natural resource management
• AI Tools and Technologies
• AI programming languages (e.g., Python)
• AI libraries and frameworks (e.g., TensorFlow, PyTorch)
• AI development environments

Module 1: Introduction to Large Language Models

• What is LLM?
• Historical evolution of LLMs
• What are the Functionality of LLM
• Real time examples of LLM
• Transform Model Architecture of LLM

Module 2: Capabilities of LLMs

• Advanced Text Generation
• Understanding Context and Nuance
• Language Translation
• Conversational AI
• Sentiment Analysis
• Code Generation and Auto-completion
• Predictive Text and Autocorrection
• Text Classification     

Module 3: Setting Up the Development Environment with LLM

• Tools and platforms for LLM development
• Basic setup for creating AI applications with LLMs

Module 4: Basic Programming with LLMs

• Introduction to programming with LLMs
• Key terms in Introduction to programming with LLMs
• Text generation, summarization with examples

Module 5: Advanced Features of LLMs

• Exploring advanced capabilities: Context retention
• Style mimicry
• Nuanced sentiment analysis
• Style transfer
• Fine-tuning and customization techniques

Module 6: Integrating LLMs in Applications

• Techniques for integrating LLMs into existing applications
• API usage and management

Module 7: Fundamentals of Finetuning

• What is Finetuning?
• Where finetuning is needed?
• How to finetune a LLM
• Compare a finetune model to a non fine tuned model
• Difference between prompt engineering and Finetuning

Module 8: Real world Use Cases

• Case Study 1: Content creation and automation

Module 9:  Real world Use Cases

• Case Study 2: Customer service chatbots

Module 10: Future Trends and Conclusion

• Emerging trends in LLMs and AI
• Discussion on the future impact of LLMs in various sectors

Module 11: Case Study: LLM Integration in Oakwood High School

• A case study about the use of a Large Language Model (LLM) in the education sector

Module 12: Best Optimization Strategies for Large Language Models

• Data Efficiency Optimization
• Model Architecture Optimization
• Training Techniques Optimization
• Resource Management Optimization
• Fine-Tuning and Generalization Optimization
• Evaluation and Testing Optimization
• Human-in-the-Loop Optimization
• Ethical Considerations Optimization
• Explainability and Interpretability Optimization
• Deployment and Scaling Optimization

Lab 1 – Compare fine-tuned vs non-finetuned models

Lab 2 – Finetune Llama 2

Lab 3 – Langchain evaluating LLM