Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to LLMs and Generative AI
- Exploring techniques and models
- Discussing applications and use cases
- Identifying challenges and limitations
Using LLMs for NLU Tasks
- Sentiment analysis
- Named entity recognition
- Relation extraction
- Semantic parsing
Using LLMs for NLI Tasks
- Entailment detection
- Contradiction detection
- Paraphrase detection
Using LLMs for Knowledge Graphs
- Extracting facts and relations from text
- Inferring missing or new facts
- Using knowledge graphs for downstream tasks
Using LLMs for Commonsense Reasoning
- Generating plausible explanations, hypotheses, and scenarios
- Using commonsense knowledge bases and datasets
- Evaluating commonsense reasoning
Using LLMs for Dialogue Generation
- Generating dialogues with conversational agents, chatbots, and virtual assistants
- Managing dialogues
- Using dialogue datasets and metrics
Using LLMs for Multimodal Generation
- Generating images from text
- Generating text from images
- Generating videos from text or images
- Generating audio from text
- Generating text from audio
- Generating 3D models from text or images
Using LLMs for Meta-Learning
- Adapting LLMs to new domains, tasks, or languages
- Learning from few-shot or zero-shot examples
- Using meta-learning and transfer learning datasets and frameworks
Using LLMs for Adversarial Learning
- Defending LLMs from malicious attacks
- Detecting and mitigating biases and errors in LLMs
- Using adversarial learning and robustness datasets and methods
Evaluating LLMs and Generative AI
- Assessing content quality and diversity
- Using metrics like inception score, Fréchet inception distance, and BLEU score
- Using human evaluation methods like crowdsourcing and surveys
- Using adversarial evaluation methods like Turing tests and discriminators
Applying Ethical Principles for LLMs and Generative AI
- Ensuring fairness and accountability
- Avoiding misuse and abuse
- Respecting the rights and privacy of content creators and consumers
- Fostering creativity and collaboration of human and AI
Summary and Next Steps
Requirements
- An understanding of basic AI concepts and terminology
- Experience with Python programming and data analysis
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch
- An understanding of the basics of LLMs and their applications
Audience
- Data scientists
- AI developers
- AI enthusiasts
21 Hours
Testimonials (1)
Exercises and exchanges during questions/answers
Antoine - Physiobotic
Course - Scaling Data Pipelines with Spark NLP
Machine Translated
