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DataCamp

Developing Large Language Models

via DataCamp

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Overview

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, like OpenAI's GPT models and Anthropic's Claude. 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.

Syllabus

  • Introduction to LLMs in Python
    • Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!
  • Working with Llama 3
    • Explore the latest techniques for running the Llama LLM locally and integrating it within your stack.
  • Natural Language Processing (NLP) in Python
    • Master text analysis with essential NLP techniques from preprocessing to advanced transformer models.
  • Transformer Models with PyTorch
    • What makes LLMs tick? Discover how transformers revolutionized text modeling and kickstarted the generative AI boom.
  • Scalable AI Models with PyTorch Lightning
    • Streamline your AI projects by building modular models and mastering advanced optimization with PyTorch Lightning!
  • 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

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