Build Solutions with Pre-trained LLMs

[mitsumi]

Build Solutions with Pre-trained LLMs

Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz
Language: English | Size: 102.08 MB | Duration: 38m 48s

This course teaches you how to build impactful, real-world solutions with large language models (LLM) that transform the way we interact with data, with hands-on guidance and key considerations on using these tools effectively and responsibly.


Large language models (LLMs) are changing the way we can interact with data, creating new interfaces for us to question and explore different forms of data, such as the internet, email, healthcare records, and more via a textual format. The field of LLMs continues to evolve rapidly, and it can be challenging to identify where to get started building solutions with LLMs and taking advantage of this revolutionary technology.
In this course, Build Solutions with Pre-trained LLMs, you'll gain the ability to implement different pre-trained language models with popular tools and frameworks, including how to customize (fine-tune), deploy, and build solutions using the models.
First, you'll explore what makes large language models big and efficient, gaining hands-on experience with popular pre-trained LLMs and using them to solve real-world problems.
Then, you'll dive into practical tools for working with pre-trained LLMs, including the HuggingFace, TensorFlow, and PyTorch libraries.
Next, you'll discover how to fine-tune pre-trained LLMs for specific tasks or domains, gaining the skills to effectively describe techniques for adapting model architectures and implementing fine-tuning.
Finally, you'll learn about the pitfalls and challenges of working with pre-trained LLMs and how to overcome them.
When you're finished with this course, you'll have the skills and knowledge needed to implement pre-trained LLMs effectively, and confidently be able to build solutions using LLMs to solve real-world problems.

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https://www.pluralsight.com/courses/pre-trained-llms-build-solutions

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