Introduction to Generative AI Transformer Models in Python

Master Transformer models in Python, learn their architecture, implement NLP applications, and fine-tune models.

Created by: Lucas Whitaker
A new instructor

What Will I Learn?

• Understand the fundamentals of generative AI Transformer models.
• Differentiate Transformer models from traditional neural networks.
• Recognize key applications of Transformer models in NLP and beyond.
• Grasp the architecture of Transformer models, including encoders and decoders.
• Implement Transformer models and applications in Python.
• Prepare data and train Transformer models effectively.
• Evaluate and analyze Transformer model performance.
• Build practical applications using transformers like text classification, language translation, and question answering.
• Fine-tune pre-trained Transformer models for specific tasks.
• Explore advanced models like BERT and GPT for practical use cases.

Requirements

• Basic understanding of Python programming.
• Interest in fundamental machine learning concepts and neural networks.
• Willingness to learn and explore Transformer models in-depth.
• Access to a computer with internet connectivity.

Target audience

• Aspiring data scientists and machine learning enthusiasts seeking to understand Transformer models.
• Software developers and engineers looking to apply advanced AI techniques in their projects.
• AI researchers and students aiming to explore state-of-the-art natural language processing techniques.
• Professionals in the field of data analysis and AI who want to enhance their skill set with modern Transformer models.
• Beginners with a basic understanding of Python and machine learning concepts, eager to learn about cutting-edge AI technologies.