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Duration 21 hours
Course Outline
Introduction to Large Language Models and Generative AI
- Examining architectural techniques and model designs
- Reviewing operational applications and sector-specific use cases
- Identifying technical constraints and operational challenges
Applying Large Language Models for Natural Language Understanding
- Sentiment analysis implementation
- Named entity recognition processes
- Relation extraction methodologies
- Semantic parsing frameworks
Applying Large Language Models for Natural Language Inference
- Entailment detection procedures
- Contradiction identification techniques
- Paraphrase recognition methods
Utilizing Large Language Models for Knowledge Graph Construction
- Extracting factual data and relational structures from text
- Inferring absent or novel factual elements
- Deploying knowledge graphs for downstream operational tasks
Employing Large Language Models for Commonsense Reasoning
Using Large Language Models for Dialogue Generation
- Creating interactions with conversational agents, chatbots, and virtual assistants
- Administering dialogue management protocols
- Utilizing dialogue datasets and performance metrics
Deploying Large Language Models for Multimodal Generation
- Synthesizing images from textual input
- Generating textual descriptions from image input
- Producing video content from text or image sources
- Generating audio output from textual prompts
- Converting audio input into text
- Constructing 3D models from text or image data
Integrating Large Language Models for Meta-Learning
- Adapting large language models to new domains, specific tasks, or languages
- Learning from limited-shot or zero-shot examples
- Applying meta-learning and transfer learning frameworks and datasets
Implementing Large Language Models for Adversarial Learning
- Safeguarding large language models against malicious intrusions
- Detecting and mitigating biases and systematic errors
- Employing adversarial learning strategies and robustness methodologies
Evaluating Large Language Models and Generative AI
- Assessing the quality and diversity of generated content
- Applying metrics such as inception score, Fréchet inception distance, and BLEU score
- Utilizing human-centric evaluation methods, including crowdsourcing and surveys
- Employing adversarial evaluation techniques, such as Turing tests and discriminators
Applying Ethical Principles to Large Language Models and Generative AI
- Ensuring fairness and institutional accountability
- Preventing misuse and improper application
- Protecting the rights and privacy of content creators and consumers
- Promoting collaborative innovation between humans and artificial intelligence
Summary and Future Directives
Requirements
- Proficiency in fundamental AI concepts and terminology
- Practical experience with Python programming and data analytics
- Familiarity with deep learning frameworks, such as TensorFlow or PyTorch
- Working knowledge of LLM fundamentals and their applicability
Target Audience
- Data Scientists
- AI Developers
- AI Enthusiasts