This module guides learners through the process of fine-tuning OpenAI GPT models, including preparing datasets in JSONL format and executing completion tasks with custom models. Learners will gain practical skills to enhance model accuracy for specific applications.

This module introduces a range of interpretability tools for transformer models, including BertViz, SHAP, LIME, LIT, and OpenAI's GPT-4 explainer. Learners will gain hands-on experience visualizing attention mechanisms, interpreting model outputs, and understanding the internal workings of large language models. By the end, you'll be equipped to make sense of complex AI systems and enhance their transparency.

This module delves into the critical role of tokenizers in transformer-based language models, examining various tokenization techniques such as subword, regular expression, and SentencePiece. Learners will explore how tokenization quality impacts model performance and discover strategies for handling out-of-vocabulary words and token-ID mapping.

This module introduces the use of large language model (LLM) embeddings as a practical alternative to fine-tuning for tasks such as retrieval-augmented generation (RAG) and question-answering. Learners will gain hands-on experience with embedding-based search, clustering techniques using Ada embeddings, and evaluating model responses without a dedicated knowledge base. The module emphasizes practical implementation and analysis of embedding-driven workflows.

This module introduces syntax-free approaches to semantic role labeling (SRL) using advanced transformer models like ChatGPT and GPT-4. Learners will explore how these models handle complex sentence structures without relying on traditional syntactic analysis, and examine the challenges and strategies for standardizing input formats in NLP tasks.

This module guides learners through the architectures and practical applications of T5 and ChatGPT for text summarization, with a focus on legal and financial domains. You will compare model configurations, implement summarization functions, and evaluate the strengths of each approach for different project needs.

This module introduces learners to the architecture and applications of Google's PaLM 2 and Vertex AI, highlighting their roles in modern natural language processing tasks. Learners will explore advanced activation functions, interface navigation, and practical use cases such as sentiment analysis and code generation. By the end, participants will gain hands-on experience leveraging these tools for real-world AI solutions.

This module examines the risks associated with large language models, including ethical concerns, memorization, influence operations, harmful content, and cybersecurity vulnerabilities. Learners will explore practical tools and strategies for risk mitigation, such as RAG, RLHF, and advanced tracking systems. By the end, participants will understand how to responsibly deploy and monitor LLMs in real-world scenarios.