Curious about how GPT, a variant of the Generative Pre-training Transformer model, generates human-like text? Join us for an insightful presentation that demystifies GPT's inner workings.

We'll explore the fundamentals of GPT, a transformer-based neural network architecture trained on vast text datasets and fine-tuned for conversational data. Learn how GPT processes prompts using its sophisticated transformer mechanism.

Dive into the specifics of GPT's vocabulary, including its size and the corpus it draws from. Understand the critical tokenization process that prepares input for the model.

The core of our presentation will focus on GPT's encoder-decoder structure, showcasing how it leverages transformer models to generate responses. We'll also cover the backpropagation algorithm, essential for training the model.

Additionally, we will emphasize the considerations necessary to improve the model's accuracy, ensuring reliable and precise text generation.

Finally, discover how GPT maintains dialogue through feedback and conversation loops, and the role of the Retrieval-Augmented Generation (RAG) model in enhancing its capabilities.

By the end of this session, you'll gain a comprehensive understanding of GPT, offering valuable insights for those interested in natural language processing and AI development.

learning objectives for the presentation:

Understand the fundamentals of GPT, including its transformer-based neural network architecture and how it generates human-like text.
Explore the specifics of GPT's vocabulary, tokenization process, and the encoder-decoder structure that powers its response generation.
Learn about the feedback and conversation loops in GPT, and the role of the Retrieval-Augmented Generation (RAG) model in enhancing its conversational capabilities.