Vendor: Cloud Contraptions
Format: Live instructor-led
Duration: 5 days
- Course details available on vendor site
- Introduction to Generative AI & LLMs
- Overview of Generative AI
- Introduction to Large Language Models (LLMs)
- Historical Perspective on Text Generation
- Use Cases and Tasks for LLMs
- Text Generation before Transformers
- N-grams and Statistical Language Models
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM) Networks
- Limitations of Pre-Transformer Models
- Transformer Architecture
- Introduction to Transformer Models
- Attention Mechanism
- Encoder-Decoder Architecture
- Self-Attention and Multi-Head Attention
- Positional Encoding
- Generating Text with Transformers
- Text Generation Techniques
- Beam Search, Sampling, and Top-k/Top-p Sampling
- Practical Examples of Text Generation
- Prompting and Prompt Engineering
- Introduction to Prompt Engineering
- Designing Effective Prompts
- Techniques for Prompt Optimization
- Examples and Best Practices
- Generative Configuration
- Model Hyperparameters
- Training Configurations
- Inference Configurations
- Fine-Tuning Configurations
- Generative AI Project Lifecycle
- Project Planning and Scoping
- Data Collection and Preprocessing
- Model Selection and Training
- Evaluation and Iteration
- Deployment and Monitoring
- Pre-training Large Language Models
- Pre-training Objectives
- Datasets for Pre-training
- Computational Challenges
- Scaling Laws and Compute-Optimal Models
- Domain Adaptation and Fine-Tuning
- Domain Adaptation Techniques
- Instruction Fine-Tuning
- Fine-Tuning on a Single Task
- Multi-Task Instruction Fine-Tuning
- Model Evaluation and Benchmarks
- Evaluation Metrics for LLMs
- Standard Benchmarks
- Evaluating Model Performance
- Parameter-Efficient Fine-Tuning (PEFT)
- Parameter Efficient Fine-Tuning (PEFT)
- Introduction to PEFT
- PEFT Techniques 1: LoRA (Low-Rank Adaptation)
- PEFT Techniques 2: Soft Prompts
- Aligning Models with Human Values
- Introduction to Model Alignment
- Reinforcement Learning from Human Feedback (RLHF)
- Obtaining Feedback from Humans
- Reward Model and Fine-Tuning with Reinforcement Learning
- Addressing Reward Hacking
- Scaling Human Feedback
- Model Optimizations for Deployment
- Model Compression Techniques
- Quantization and Pruning
- Optimizing Inference Performance
- Deployment Strategies
- Generative AI Project Lifecycle Cheat Sheet
- Quick Reference Guide for Project Lifecycle
- Key Steps and Best Practices
- Common Pitfalls and Solutions
- Using the LLM in Applications
- Integrating LLMs into Applications
- Interacting with External Applications
- Helping LLMs Reason and Plan with Chain-of-Thought
- Advanced Techniques and Applications
- Program-Aided Language Models (PAL)
- ReAct: Combining Reasoning and Action
- LLM Application Architectures
- Responsible AI
- Ethical Considerations in Generative AI
- Bias and Fairness in LLMs
- Privacy and Security Concerns
- Developing Responsible AI Practices
- Conclusion
- Recap of Key Concepts
- Q&A Session
- Next Steps and Future Trends
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- We specialize in delivering computer programming training and consulting services with an emphasis on using the latest in AI technology. We are customer-friendly and employ AI best practices and teach these practices to companies around the world.
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- Contact Info
- +1 434-945-9416
- help@cloudcontraptions.com
- 3378 South Amherst Hwy
- Unit 21
- Monroe, VA 24574
- © 2026 Cloud Contraptions LLC. All Rights Reserved.
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- No generative AI experience is required. Experience with Python is required.
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Generated on 2026-04-16 03:36:22