Advanced AI Development
This course covers advanced topics like fine-tuning GPT and AUTO-GPT models, understanding transformer architecture, using Langchain and Pandas for complex tasks, and exploring the latest research in the field.
Prerequisites
- A solid appreciation of AI technology
Contents
Review of AI Development Basics
- Recap of fundamental AI, Machine Learning and Deep Learning concepts
- Review of GPT and AUTO-GPT models
- Overview of AI development tools and libraries
Advanced Topics in AI Development
- Hyperparameter tuning in Machine Learning and Deep Learning
- Advanced techniques in feature engineering and data augmentation
- Deep dive into transformer models and advanced architectures
- Understanding Capsule Networks and Graph Neural Networks
Advanced AUTO-GPT Applications
- Building complex applications with AUTO-GPT
- AUTO-GPT in multi-turn conversations and dialog systems
- AUTO-GPT in advanced NLP tasks: summarization, translation, sentiment analysis
- Troubleshooting and optimizing AUTO-GPT applications
Advanced Prompt Engineering
- Advanced techniques for effective prompt design
- Prompt engineering for advanced tasks and complex domains
- Handling biases and ethical issues in advanced prompt engineering
Vector Databases in AI Development
- Deep dive into vector databases
- Use-cases and applications of vector databases in AI
- Best practices when using vector databases
AI Ethics and Fairness
- Advanced topics in AI Ethics: interpretability, explainability, accountability
- Understanding and mitigating bias in advanced AI models
- The role of regulation in AI
AI System Design and Architecture
- Designing robust and scalable AI systems
- Advanced topics in AI system architecture
- Designing AI systems for security and privacy
Deploying and Maintaining AI Applications
- Advanced topics in AI application deployment
- Monitoring and maintaining AI systems in production
- Ensuring the reliability and robustness of AI
