Start your journey to developing Large Language Models (LLMs) today! In this track, you'll learn about the latest techniques for developing state-of-the-art language models responsible for the recent boom in generative AI models, like OpenAI's GPT-4, and Meta's Llama 3.
Master deep learning with PyTorch to discover how neural networks can be used to model patterns in unstructured data, such as text. Discover how the transformers architecture has revolutionized text modeling, and how to leverage the variety of pre-trained LLMs available from Hugging Face. Finally, learn about the challenges and strategies involved in building and deploying LLMs, from inception to real-world application.
Overview
Syllabus
- Introduction to LLMs in Python
- Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
- Analyzing Car Reviews with LLMs
- Working with Llama 3
- Explore the latest techniques for running the Llama LLM locally and integrating it within your stack.
- Classifying Emails using Llama
- Deep Learning for Text with PyTorch
- Discover the exciting world of Deep Learning for Text with PyTorch and unlock new possibilities in natural language processing and text generation.
- Service Desk Ticket Classification with Deep Learning
- Transformer Models with PyTorch
- What makes LLMs tick? Discover how transformers revolutionized text modeling and kickstarted the generative AI boom.
- Reinforcement Learning from Human Feedback (RLHF)
- Learn how to make GenAI models truly reflect human values while gaining hands-on experience with advanced LLMs.
- LLMOps Concepts
- Learn about LLMOps from ideation to deployment, gain insights into the lifecycle and challenges, and learn how to apply these concepts to your applications.
Taught by
Maham Khan, Thomas Hossler, Shubham Jain, Michał Oleszak, Jasmin Ludolf, Iván Palomares Carrascosa, Max Knobbout, and Imtihan Ahmed
