Advanced AI Development training course

 Unit 1: Review of AI Development Basics

• 1.1 Recap of fundamental AI, Machine Learning and Deep Learning concepts
• 1.2 Review of GPT and AUTO-GPT models
• 1.3 Overview of AI development tools and libraries

Unit 2: Advanced Topics in AI Development

• 2.1 Hyperparameter tuning in Machine Learning and Deep Learning
• 2.2 Advanced techniques in feature engineering and data augmentation
• 2.3 Deep dive into transformer models and advanced architectures
• 2.4 Understanding Capsule Networks and Graph Neural Networks

Unit 3: Advanced AUTO-GPT Applications

• 3.1 Building complex applications with AUTO-GPT
• 3.2 AUTO-GPT in multi-turn conversations and dialog systems
• 3.3 AUTO-GPT in advanced NLP tasks: summarization, translation, sentiment analysis
• 3.4 Troubleshooting and optimizing AUTO-GPT applications

Unit 4: Advanced Prompt Engineering

• 4.1 Advanced techniques for effective prompt design
• 4.2 Prompt engineering for advanced tasks and complex domains
• 4.3 Handling biases and ethical issues in advanced prompt engineering

Unit 5: Vector Databases in AI Development

• 5.1 Deep dive into vector databases
• 5.2 Use-cases and applications of vector databases in AI
• 5.3 Best practices when using vector databases

Unit 6: AI Ethics and Fairness

• 6.1 Advanced topics in AI Ethics: interpretability, explainability, accountability
• 6.2 Understanding and mitigating bias in advanced AI models
• 6.3 The role of regulation in AI

Unit 7: AI System Design and Architecture

• 7.1 Designing robust and scalable AI systems
• 7.2 Advanced topics in AI system architecture
• 7.3 Designing AI systems for security and privacy

Unit 8: Deploying and Maintaining AI Applications

• 8.1 Advanced topics in AI application deployment
• 8.2 Monitoring and maintaining AI systems in production
• 8.3 Ensuring the reliability and robustness of AI applications