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Foundations of Large Language Models: Tools, Techniques, and Applications

Technologies like OpenAI’s ChatGPT and Google’s Bard are changing the way we work and generative AI will become increasingly popular. As data professionals and developers, it is…

Technologies like OpenAI’s ChatGPT and Google’s Bard are changing the way we work and generative AI will become increasingly popular. As data professionals and developers, it is important to know how these tools work under the hood and how you can leverage large language models (LLMs) for your work.

LLMs have revolutionized the field of natural language processing (NLP) and are increasingly being used to solve a wide range of NLP problems in various industries. Understanding LLMs can help developers and data scientists, like you, to:

  • Build better NLP models: LLMs are state-of-the-art models for many NLP tasks, and understanding how they work can help developers and data scientists to build better models and achieve better performance on their NLP tasks.
  • Develop custom NLP applications: LLMs can be fine-tuned to specific NLP tasks, making them highly adaptable to different domains and use cases. Developers and data scientists who understand LLMs can leverage this flexibility to develop custom NLP applications for their specific needs.
  • Optimize model performance: Understanding LLMs can help developers and data scientists to optimize model performance by selecting the appropriate architecture, prompt engineering, fine-tuning strategies, and downstream tasks for their specific use case.

With the most recent release of OpenAI’s GPT-4 language model, it is being used by Morgan Stanley wealth management to organize its vast knowledge base, Be My Eyes to transform visual accessibility, Stripe to streamline user experinece and combat fraud, and the Government of Iceland to preserve its language. 

This course will provide you with a comprehensive understanding of the latest techniques, tools, and applications of LLMs so you can build applications or processes and further improve your effectiveness and efficiency when working with large language models.